Amazon text and image coverage

This audit analyzes cached item metadata for all 33 named Amazon subsets and their four verified split profiles. Its curated text recipes are now installed as per-subset preprocessing defaults for all four split strategies. The change does not download images, create embeddings, or evaluate recommendation quality. All 33 subsets / 132 profiles completed the audit on 9 September 2026. The machine-readable coverage archive contains the full counts, word-length quantiles, thresholds 1/5/10/20/30/50/100, code fingerprints and source-cache file identities. Metadata cache sizes and modification times match the original sealed-input manifests; retained catalogs were rebuilt against the same checksum-verified inputs and frozen split code.

What the parameters actually do

Amazon defaults to the category-specific metadata_text_fields listed under How to assemble text and min_entity_text_words=0. The latter disables text-length filtering; an item can have no description or even empty combined text and still be included, provided its metadata record exists and it survives support pruning.

metadata_text_fields selects top-level fields and nested dictionary paths such as details.Brand in the requested order. Keys are case-sensitive; missing or empty values are skipped. Adjacent nested selections share a single Details: label. JSON-encoded details and Parquet text arrays are supported. An explicit field list replaces the category default; it is not appended to it. Nonempty fields are joined into entity_text with field labels and blank lines. A positive min_entity_text_words counts whitespace-separated words in that combined string, including labels. It is not a description-presence test, an encoder-token limit, or a semantic-quality measure. For example, a title and categories can pass the threshold while description is completely absent.

Metadata filtering happens before support filtering and splitting. Removing short-text items removes their interactions, potentially causing additional users and items to fail support thresholds. The audit’s threshold-loss figures are direct losses on the existing fixed catalogs/matrices, not the sizes of newly pruned or re-split checkpoints. For temporal, re-filtering can also change timestamp endpoints and windows; it is not safe to reuse the old measured sizes.

Coverage definitions

The category summary uses the union of retained item IDs across the four split profiles, including the preprocessed catalogs for non-temporal profiles. It is not the full raw Amazon catalog and does not weight popular items more heavily. The split-level appendix separately measures the final catalog, observed training items, validation targets and test targets. Training-pair loss is weighted by unique nonzero user-item pairs in the original training matrix.

Text means nonempty combined installed-default text after normalizing array containers; description and features mean nonempty content in those individual fields. These are availability checks, not guarantees that the text is informative. Images means at least one recorded HTTP(S) product-metadata image URL. URLs were not fetched: reachability, actual image contents, resolution, duplicates and encoder success are unverified. Review text and customer-uploaded images are not used. The upstream field definitions distinguish product metadata from review content.

Measured coverage by subset

The coverage and loss columns below use the installed curated recipes, as measured in the original audit’s curated result. The historical four-field baseline is retained in the comparison tables and raw archive. Image URLs reflect normalized source contents, not the adapter’s current Parquet limitation. The union counts may exceed a single split’s size cap because the split catalogs overlap only partially; no individual profile’s size limits have changed.

Installed-default metadata coverage and direct text-filter loss

Subset	Union items	Text	Description	Features	Image URLs	Below 10 words	Below 30 words
All_Beauty	18,755	100.00%	15.71%	18.36%	100.00%	1.21%	40.47%
Amazon_Fashion	13,201	100.00%	10.86%	61.76%	100.00%	0.82%	72.44%
Appliances	22,334	100.00%	57.35%	90.32%	100.00%	0.05%	3.56%
Arts_Crafts_and_Sewing	19,690	100.00%	55.59%	94.95%	99.95%	0.01%	0.69%
Automotive	20,809	100.00%	62.91%	97.54%	>99.99%	<0.01%	0.45%
Baby_Products	20,108	100.00%	57.30%	94.86%	99.99%	0.02%	1.36%
Beauty_and_Personal_Care	19,153	100.00%	49.47%	90.95%	100.00%	0.00%	0.72%
Books	127,961	100.00%	77.61%	98.76%	88.66%	0.04%	0.39%
CDs_and_Vinyl	23,072	100.00%	90.69%	0.59%	99.92%	0.00%	8.43%
Cell_Phones_and_Accessories	23,458	100.00%	34.99%	91.99%	100.00%	0.02%	0.74%
Clothing_Shoes_and_Jewelry	24,842	100.00%	38.57%	99.56%	99.99%	0.00%	0.14%
Digital_Music	18,995	100.00%	42.30%	0.09%	100.00%	10.73%	63.81%
Electronics	99,261	100.00%	55.74%	92.28%	>99.99%	0.02%	1.16%
Gift_Cards	749	100.00%	77.57%	91.72%	100.00%	0.13%	3.74%
Grocery_and_Gourmet_Food	21,273	100.00%	70.13%	94.01%	99.95%	0.03%	1.30%
Handmade_Products	23,704	100.00%	81.05%	56.46%	100.00%	<0.01%	2.13%
Health_and_Household	23,856	100.00%	53.95%	96.78%	100.00%	0.00%	0.16%
Health_and_Personal_Care	17,288	100.00%	28.08%	28.31%	100.00%	2.54%	45.14%
Home_and_Kitchen	18,990	100.00%	60.65%	98.35%	100.00%	0.00%	0.18%
Industrial_and_Scientific	22,281	100.00%	54.32%	93.18%	100.00%	0.01%	0.86%
Kindle_Store	23,549	100.00%	68.42%	99.96%	89.32%	0.00%	<0.01%
Magazine_Subscriptions	2,003	100.00%	42.69%	0.00%	100.00%	6.64%	57.96%
Movies_and_TV	24,791	100.00%	51.55%	6.03%	100.00%	0.63%	33.02%
Musical_Instruments	20,065	100.00%	67.55%	95.38%	99.98%	0.04%	1.49%
Office_Products	22,096	100.00%	56.08%	96.37%	99.70%	<0.01%	0.33%
Patio_Lawn_and_Garden	17,357	100.00%	57.25%	96.79%	99.97%	0.00%	0.14%
Pet_Supplies	22,828	100.00%	59.58%	96.82%	99.98%	0.01%	0.49%
Software	18,322	100.00%	98.49%	99.25%	>99.99%	0.02%	0.88%
Sports_and_Outdoors	25,641	100.00%	50.72%	97.30%	100.00%	0.02%	0.40%
Subscription_Boxes	327	99.69%	0.00%	96.33%	100.00%	0.31%	7.03%
Tools_and_Home_Improvement	19,936	100.00%	62.86%	97.73%	>99.99%	0.00%	0.21%
Toys_and_Games	23,646	100.00%	67.03%	98.88%	>99.99%	<0.01%	0.18%
Video_Games	23,083	100.00%	69.08%	89.16%	>99.99%	0.11%	2.80%

Source coverage versus current loader behavior

The cached Hugging Face Parquet categories encode image records as a dictionary of array columns. The current adapter expects a list of dictionaries with scalar URL strings, so it fails to expose these URLs through include_image_urls=True. This is a loader limitation, not absent images. Parquet text-list cells also arrive as NumPy arrays. Text-array handling is now fixed: the formatter joins their contents and skips empty arrays, just as it does for JSON lists. It previously stringified arrays, including brackets and empty [] values.

The table below preserves the audit’s pre-change word counts and normalized four-field comparison. The installed defaults additionally include curated attributes; their results appear in the other tables. The image-loader issues remain unresolved and need correction before image embedding builds. Positive text thresholds require a fresh split verification; the default remains zero.

Prime Video records inside Movies and TV add a second format: their image keys use width names such as 720w and 1920w, which the current extractor also ignores. Source coverage in this report includes all recorded HTTP(S) image variants, including those widths; the preliminary 47.63% estimate based only on hi_res/large/thumb was incomplete and is superseded by this corrected audit.

Historical four-field baseline: source versus pre-change adapter

Subset	Source image URLs	Adapter image URLs	Text affected by old array formatting	Below 30: old native	Below 30: normalized four fields
All_Beauty	100.00%	100.00%	0.00%	66.64%	66.64%
Amazon_Fashion	100.00%	100.00%	0.00%	76.58%	76.58%
Appliances	100.00%	100.00%	0.00%	5.40%	5.40%
Arts_Crafts_and_Sewing	99.95%	0.00%	100.00%	1.14%	1.38%
Automotive	>99.99%	>99.99%	0.00%	1.12%	1.12%
Baby_Products	99.99%	99.99%	0.00%	2.47%	2.47%
Beauty_and_Personal_Care	100.00%	100.00%	0.00%	3.24%	3.24%
Books	88.66%	88.66%	0.00%	1.37%	1.37%
CDs_and_Vinyl	99.92%	99.92%	0.00%	18.64%	18.64%
Cell_Phones_and_Accessories	100.00%	0.00%	100.00%	1.51%	2.27%
Clothing_Shoes_and_Jewelry	99.99%	99.99%	0.00%	0.40%	0.40%
Digital_Music	100.00%	100.00%	0.00%	72.51%	72.51%
Electronics	>99.99%	0.00%	100.00%	2.06%	2.34%
Gift_Cards	100.00%	0.00%	100.00%	4.67%	4.67%
Grocery_and_Gourmet_Food	99.95%	99.95%	0.00%	2.92%	2.92%
Handmade_Products	100.00%	0.00%	100.00%	2.89%	3.09%
Health_and_Household	100.00%	100.00%	0.00%	0.73%	0.73%
Health_and_Personal_Care	100.00%	100.00%	0.00%	60.02%	60.02%
Home_and_Kitchen	100.00%	100.00%	0.00%	0.39%	0.39%
Industrial_and_Scientific	100.00%	0.00%	100.00%	1.48%	1.69%
Kindle_Store	89.32%	89.32%	0.00%	0.06%	0.06%
Magazine_Subscriptions	100.00%	100.00%	0.00%	60.11%	60.11%
Movies_and_TV	100.00%	47.63%	0.00%	50.09%	50.09%
Musical_Instruments	99.98%	0.00%	100.00%	1.90%	2.10%
Office_Products	99.70%	99.70%	0.00%	0.81%	0.81%
Patio_Lawn_and_Garden	99.97%	99.97%	0.00%	0.55%	0.55%
Pet_Supplies	99.98%	99.98%	0.00%	1.00%	1.00%
Software	>99.99%	>99.99%	0.00%	1.20%	1.20%
Sports_and_Outdoors	100.00%	100.00%	0.00%	1.01%	1.01%
Subscription_Boxes	100.00%	100.00%	0.00%	7.03%	7.03%
Tools_and_Home_Improvement	>99.99%	>99.99%	0.00%	0.57%	0.57%
Toys_and_Games	>99.99%	0.00%	100.00%	0.35%	0.38%
Video_Games	>99.99%	>99.99%	0.00%	4.06%	4.06%

How to assemble text

Keep the title as the compact identity anchor. Add features and descriptions when present; they often complement each other rather than supplying interchangeable coverage. Categories add taxonomy when present, but cannot compensate for missing product-specific content. A store name can help identify brand, artist or author, but must be inspected: it is not universally equivalent to any of those fields.

The subset recipes below are installed defaults, selected for semantic content and coverage, not validated for recommendation quality. Missing optional fields are skipped. The order prioritizes store/creator context for media, then whichever prose field has wider coverage; absent fields are omitted. All other nonempty optional prose is kept. store needs category-specific cleanup: inspected Books entries include author names and format labels, while other domains use brands or storefronts. Inspected Books features include substantial synopses, so they must not be discarded merely because the field name suggests short specification bullets. There is no reason to change a split’s support parameters merely to change field ordering while min_entity_text_words=0.

Movies and TV particularly needs curated nested attributes: many Prime Video records have no title, features, description or category list, but do have details.Directors, details.Starring, audio languages and subtitles. These provide a meaningful attribute fallback, not a recovered title or synopsis. Just reordering the four existing fields cannot recover absent content. Across the retained Movies and TV union, the original four-field text was nonempty for only 53.65% of items; the installed curated recipe reaches 100%. Corrected source image-URL coverage is also 100%, while the current adapter exposes only 47.63%. Books and Kindle Store have genuine source-URL gaps: their coverage is 88.66% and 89.32%, respectively. None of these URLs has been fetched in this audit.

Installed per-subset metadata_text_fields (both columns, in order)

Subset	Top-level fields	Included nested detail fields
All_Beauty	title,features,description,store	details.Brand; details.Item Form; details.Skin Type; details.Hair Type; details.Product Benefits
Amazon_Fashion	title,features,description,store	details.Department; details.Brand; details.Material; details.Color; details.Style
Appliances	title,features,description,categories,store	details.Brand; details.Compatible Devices; details.Capacity; details.Voltage; details.Material
Arts_Crafts_and_Sewing	title,features,description,categories,store	details.Brand; details.Material; details.Color; details.Size; details.Paint Type
Automotive	title,features,description,categories,store	details.Brand; details.Vehicle Service Type; details.Auto Part Position; details.Material
Baby_Products	title,features,description,categories,store	details.Brand; details.Age Range (Description); details.Material; details.Style
Beauty_and_Personal_Care	title,features,description,categories,store	details.Brand; details.Item Form; details.Skin Type; details.Scent; details.Product Benefits
Books	title,store,features,description,categories	details.Publisher; details.Language; details.Paperback; details.Hardcover
CDs_and_Vinyl	title,store,description,features,categories	details.Label; details.Language; details.Media Format; details.Run time
Cell_Phones_and_Accessories	title,features,description,categories,store	details.Brand; details.Compatible Devices; details.Compatible Phone Models; details.Material
Clothing_Shoes_and_Jewelry	title,features,description,categories,store	details.Department; details.Brand; details.Material; details.Color; details.Style
Digital_Music	title,store,description,features,categories	details.Label; details.Genres; details.Language; details.Run time
Electronics	title,features,description,categories,store	details.Brand; details.Compatible Devices; details.Connectivity Technology; details.Screen Size; details.Memory Storage Capacity
Gift_Cards	title,features,description,categories,store	details.Brand; details.Manufacturer
Grocery_and_Gourmet_Food	title,features,description,categories,store	details.Brand; details.Flavor; details.Item Form; details.Specialty; details.Allergen Information
Handmade_Products	title,description,features,categories,store	details.Material; details.Color; details.Size; details.Department
Health_and_Household	title,features,description,categories,store	details.Brand; details.Item Form; details.Active Ingredients; details.Material; details.Scent
Health_and_Personal_Care	title,features,description,store	details.Brand; details.Item Form; details.Active Ingredients; details.Skin Type
Home_and_Kitchen	title,features,description,categories,store	details.Brand; details.Material; details.Color; details.Style; details.Size
Industrial_and_Scientific	title,features,description,categories,store	details.Brand; details.Material; details.Size; details.Compatible Devices
Kindle_Store	title,store,features,description,categories	details.Publisher; details.Language; details.Print length; details.Author
Magazine_Subscriptions	title,store,description,categories	details.Publisher; details.Language; details.Print length
Movies_and_TV	title,store,description,features,categories	details.Director; details.Directors; details.Actors; details.Starring; details.Media Format; details.Language; details.Audio languages; details.Subtitles; details.Run time
Musical_Instruments	title,features,description,categories,store	details.Brand; details.Instrument; details.Material; details.Compatible Devices
Office_Products	title,features,description,categories,store	details.Brand; details.Material; details.Color; details.Sheet Size; details.Ink Color
Patio_Lawn_and_Garden	title,features,description,categories,store	details.Brand; details.Material; details.Power Source; details.Color
Pet_Supplies	title,features,description,categories,store	details.Brand; details.Target Species; details.Breed Recommendation; details.Flavor; details.Material
Software	title,store,features,description,categories	details.Platform; details.Operating System; details.Manufacturer; details.Language
Sports_and_Outdoors	title,features,description,categories,store	details.Brand; details.Sport; details.Material; details.Size; details.Age Range (Description)
Subscription_Boxes	title,features,store	details.Brand; details.Manufacturer; details.Material
Tools_and_Home_Improvement	title,features,description,categories,store	details.Brand; details.Material; details.Power Source; details.Voltage
Toys_and_Games	title,features,description,categories,store	details.Brand; details.Age Range (Description); details.Material; details.Theme; details.Educational Objective
Video_Games	title,store,features,description,categories	details.Platform; details.Rated; details.Manufacturer; details.Language

Do not concatenate all details indiscriminately. The measured dictionaries include useful category-specific attributes, but also identifiers, packaging, dates and, in many subsets, Best Sellers Rank. Exclude popularity aggregates (average_rating, rating_number and ranks), IDs such as ASIN/ISBN/UPC, purchase/co-purchase links, and irrelevant shipping boilerplate from semantic text. Snapshot popularity can reveal post-split information and confound a content-only or temporal experiment. Even ordinary descriptions are crawl-time snapshots, not historically versioned metadata.

The recipe table lists the full installed selection, not only frequently observed keys. Both columns form one ordered metadata_text_fields tuple. The same category recipe applies to user_split, item_split, leave_last_out and temporal; it does not modify their support thresholds, holdout sizes or rating policy. Unknown/unprofiled categories fall back to title,features,description,categories. Existing checkpoints are not rewritten; rebuild one to obtain the enriched entity_text. The resolved field list is recorded in the new checkpoint’s data-stage manifest.

To replace the default explicitly, for example:

cr.build_recsys_checkpoint(
    dataset="amazon2023",
    amazon_category="Movies_and_TV",
    metadata_text_fields=["title", "description", "details.Directors", "details.Starring"],
    min_entity_text_words=0,
)

An explicit top-level details still includes the entire dictionary. That is not the default: it can raise word counts with identifiers or popularity data. Further whitespace/HTML cleanup, deduplication and encoder truncation remain embedding-pipeline recommendations, not part of these measured word counts.

The next table compares the original four-field baseline with the installed curated recipe: normalized text containers, available prose/taxonomy fields, store where observed, and the category’s allowlisted details. It uses the original audit’s curated counts; regression tests check equivalent word counts for all 33 recipes. Field order alone does not change these counts. More nonempty or longer text does not itself prove better recommendation quality.

Measured effect of the installed curated defaults

Subset	Original four-field text coverage	Installed text coverage	Installed items below 30	Original train-pair loss at 30: range over splits	Installed train-pair loss at 30: range over splits
All_Beauty	>99.99%	100.00%	40.47%	56.21%–63.96%	33.50%–35.27%
Amazon_Fashion	>99.99%	100.00%	72.44%	66.35%–75.36%	60.25%–70.13%
Appliances	100.00%	100.00%	3.56%	1.93%–2.29%	1.27%–1.50%
Arts_Crafts_and_Sewing	100.00%	100.00%	0.69%	0.71%–0.98%	0.42%–0.57%
Automotive	100.00%	100.00%	0.45%	0.68%–0.79%	0.27%–0.32%
Baby_Products	100.00%	100.00%	1.36%	0.87%–1.31%	0.40%–0.67%
Beauty_and_Personal_Care	100.00%	100.00%	0.72%	2.55%–3.01%	0.61%–0.72%
Books	100.00%	100.00%	0.39%	0.54%–0.64%	0.14%–0.18%
CDs_and_Vinyl	100.00%	100.00%	8.43%	13.63%–15.02%	6.42%–6.61%
Cell_Phones_and_Accessories	100.00%	100.00%	0.74%	0.78%–1.21%	0.34%–0.58%
Clothing_Shoes_and_Jewelry	100.00%	100.00%	0.14%	0.40%–0.45%	0.07%–0.10%
Digital_Music	>99.99%	100.00%	63.81%	45.40%–72.12%	36.12%–62.42%
Electronics	100.00%	100.00%	1.16%	0.97%–1.27%	0.61%–0.76%
Gift_Cards	100.00%	100.00%	3.74%	0.97%–1.19%	0.74%–0.88%
Grocery_and_Gourmet_Food	100.00%	100.00%	1.30%	2.06%–2.16%	0.87%–0.95%
Handmade_Products	100.00%	100.00%	2.13%	1.39%–2.56%	0.94%–1.81%
Health_and_Household	100.00%	100.00%	0.16%	0.54%–0.65%	0.13%–0.15%
Health_and_Personal_Care	99.98%	100.00%	45.14%	48.57%–53.01%	30.43%–34.11%
Home_and_Kitchen	100.00%	100.00%	0.18%	0.16%–0.24%	0.07%–0.11%
Industrial_and_Scientific	100.00%	100.00%	0.86%	0.73%–0.96%	0.36%–0.50%
Kindle_Store	100.00%	100.00%	<0.01%	0.07%–0.09%	0.02%–0.02%
Magazine_Subscriptions	100.00%	100.00%	57.96%	59.52%–63.15%	59.04%–62.26%
Movies_and_TV	53.65%	100.00%	33.02%	51.54%–62.21%	26.62%–36.33%
Musical_Instruments	100.00%	100.00%	1.49%	0.88%–1.10%	0.62%–0.78%
Office_Products	100.00%	100.00%	0.33%	0.37%–0.47%	0.21%–0.26%
Patio_Lawn_and_Garden	100.00%	100.00%	0.14%	0.43%–0.54%	0.12%–0.14%
Pet_Supplies	>99.99%	100.00%	0.49%	0.41%–0.56%	0.15%–0.22%
Software	100.00%	100.00%	0.88%	0.47%–0.80%	0.39%–0.71%
Sports_and_Outdoors	100.00%	100.00%	0.40%	0.52%–0.71%	0.20%–0.30%
Subscription_Boxes	99.69%	99.69%	7.03%	4.51%–8.82%	4.51%–8.82%
Tools_and_Home_Improvement	100.00%	100.00%	0.21%	0.32%–0.41%	0.11%–0.15%
Toys_and_Games	100.00%	100.00%	0.18%	0.17%–0.20%	0.05%–0.10%
Video_Games	100.00%	100.00%	2.80%	2.18%–2.28%	1.63%–1.72%

For limited encoder context, preserve title and selected category-specific attributes, then allocate a budget to features and description. Use the actual encoder tokenizer to set that budget; whitespace word counts are not token counts. Record field order, normalization, selected attribute keys, encoder and truncation policy alongside embeddings so experiments remain reproducible.

Recommendation

Keep min_entity_text_words=0 for the common collaborative/multimodal benchmark. Preserve the verified interactions and splits, then record text/image availability masks with embeddings. Evaluate content-only methods on explicitly defined available-feature cohorts and report excluded coverage, or use a documented fallback. Do not silently drop users or items only for one competing method.

If a text-only benchmark genuinely requires a minimum length, choose it per subset from the measured loss curves, rebuild support pruning and every split, and recheck eligible validation/test users. A threshold of 1 tests for some combined text; neither 1 nor 30 guarantees a useful description. Real descriptions should be checked directly when that is the scientific requirement.

Detailed split measurements

Text coverage and direct threshold losses below use the installed curated recipes. With the default threshold of zero, no interactions are removed for text length; the user/item counts and verified split profiles are unchanged.

Per-split installed-default coverage and direct training-pair loss

Subset	Split	Catalog: items / text / image	Train: items / text / image	Validation targets: items / text / image	Test targets: items / text / image	Train pairs lost below 1 / 10 / 30 words
All_Beauty	user_split	11,834 / 100.00% / 100.00%	11,834 / 100.00% / 100.00%	1,826 / 100.00% / 100.00%	1,854 / 100.00% / 100.00%	0.00% / 1.01% / 34.73%
All_Beauty	item_split	12,105 / 100.00% / 100.00%	10,288 / 100.00% / 100.00%	602 / 100.00% / 100.00%	1,203 / 100.00% / 100.00%	0.00% / 1.01% / 35.27%
All_Beauty	leave_last_out	10,266 / 100.00% / 100.00%	7,242 / 100.00% / 100.00%	2,503 / 100.00% / 100.00%	2,487 / 100.00% / 100.00%	0.00% / 0.99% / 34.46%
All_Beauty	temporal	10,734 / 100.00% / 100.00%	7,173 / 100.00% / 100.00%	1,657 / 100.00% / 100.00%	618 / 100.00% / 100.00%	0.00% / 0.94% / 33.50%
Amazon_Fashion	user_split	10,206 / 100.00% / 100.00%	10,206 / 100.00% / 100.00%	2,779 / 100.00% / 100.00%	2,692 / 100.00% / 100.00%	0.00% / 0.76% / 64.26%
Amazon_Fashion	item_split	10,217 / 100.00% / 100.00%	8,684 / 100.00% / 100.00%	511 / 100.00% / 100.00%	1,022 / 100.00% / 100.00%	0.00% / 0.73% / 64.24%
Amazon_Fashion	leave_last_out	4,098 / 100.00% / 100.00%	3,788 / 100.00% / 100.00%	1,292 / 100.00% / 100.00%	1,190 / 100.00% / 100.00%	0.00% / 0.91% / 70.13%
Amazon_Fashion	temporal	4,080 / 100.00% / 100.00%	2,933 / 100.00% / 100.00%	673 / 100.00% / 100.00%	467 / 100.00% / 100.00%	0.00% / 0.90% / 60.25%
Appliances	user_split	19,078 / 100.00% / 100.00%	19,078 / 100.00% / 100.00%	3,391 / 100.00% / 100.00%	3,277 / 100.00% / 100.00%	0.00% / <0.01% / 1.46%
Appliances	item_split	19,327 / 100.00% / 100.00%	16,427 / 100.00% / 100.00%	967 / 100.00% / 100.00%	1,933 / 100.00% / 100.00%	0.00% / <0.01% / 1.49%
Appliances	leave_last_out	10,896 / 100.00% / 100.00%	9,603 / 100.00% / 100.00%	6,274 / 100.00% / 100.00%	6,215 / 100.00% / 100.00%	0.00% / <0.01% / 1.50%
Appliances	temporal	16,820 / 100.00% / 100.00%	10,232 / 100.00% / 100.00%	8,307 / 100.00% / 100.00%	7,199 / 100.00% / 100.00%	0.00% / 0.01% / 1.27%
Arts_Crafts_and_Sewing	user_split	18,043 / 100.00% / 99.95%	18,043 / 100.00% / 99.95%	6,506 / 100.00% / 99.97%	6,613 / 100.00% / 99.95%	0.00% / <0.01% / 0.53%
Arts_Crafts_and_Sewing	item_split	18,043 / 100.00% / 99.95%	15,335 / 100.00% / 99.94%	903 / 100.00% / 100.00%	1,805 / 100.00% / 100.00%	0.00% / 0.01% / 0.49%
Arts_Crafts_and_Sewing	leave_last_out	18,043 / 100.00% / 99.95%	18,043 / 100.00% / 99.95%	16,863 / 100.00% / 99.95%	16,459 / 100.00% / 99.97%	0.00% / <0.01% / 0.57%
Arts_Crafts_and_Sewing	temporal	12,713 / 100.00% / 99.97%	8,849 / 100.00% / 99.97%	9,307 / 100.00% / 99.97%	9,221 / 100.00% / 99.98%	0.00% / <0.01% / 0.42%
Automotive	user_split	16,079 / 100.00% / >99.99%	16,079 / 100.00% / >99.99%	7,492 / 100.00% / 99.99%	7,574 / 100.00% / 99.99%	0.00% / <0.01% / 0.30%
Automotive	item_split	16,079 / 100.00% / >99.99%	13,667 / 100.00% / >99.99%	804 / 100.00% / 100.00%	1,608 / 100.00% / 100.00%	0.00% / <0.01% / 0.28%
Automotive	leave_last_out	16,079 / 100.00% / >99.99%	16,079 / 100.00% / >99.99%	15,101 / 100.00% / >99.99%	14,639 / 100.00% / >99.99%	0.00% / <0.01% / 0.32%
Automotive	temporal	19,182 / 100.00% / >99.99%	13,869 / 100.00% / >99.99%	15,181 / 100.00% / >99.99%	12,891 / 100.00% / >99.99%	0.00% / <0.01% / 0.27%
Baby_Products	user_split	17,987 / 100.00% / 99.98%	17,987 / 100.00% / 99.98%	6,029 / 100.00% / 99.98%	6,056 / 100.00% / 99.98%	0.00% / 0.01% / 0.65%
Baby_Products	item_split	17,987 / 100.00% / 99.98%	15,288 / 100.00% / 99.99%	900 / 100.00% / 100.00%	1,799 / 100.00% / 99.94%	0.00% / <0.01% / 0.57%
Baby_Products	leave_last_out	17,987 / 100.00% / 99.98%	17,983 / 100.00% / 99.98%	14,115 / 100.00% / 99.99%	13,560 / 100.00% / 99.98%	0.00% / 0.01% / 0.67%
Baby_Products	temporal	13,365 / 100.00% / 99.98%	9,096 / 100.00% / 99.97%	7,822 / 100.00% / 99.97%	8,198 / 100.00% / 99.98%	0.00% / <0.01% / 0.40%
Beauty_and_Personal_Care	user_split	16,817 / 100.00% / 100.00%	16,817 / 100.00% / 100.00%	7,981 / 100.00% / 100.00%	8,008 / 100.00% / 100.00%	0.00% / 0.00% / 0.69%
Beauty_and_Personal_Care	item_split	16,817 / 100.00% / 100.00%	14,294 / 100.00% / 100.00%	841 / 100.00% / 100.00%	1,682 / 100.00% / 100.00%	0.00% / 0.00% / 0.63%
Beauty_and_Personal_Care	leave_last_out	16,817 / 100.00% / 100.00%	16,817 / 100.00% / 100.00%	15,180 / 100.00% / 100.00%	14,750 / 100.00% / 100.00%	0.00% / 0.00% / 0.72%
Beauty_and_Personal_Care	temporal	14,656 / 100.00% / 100.00%	8,247 / 100.00% / 100.00%	9,685 / 100.00% / 100.00%	11,789 / 100.00% / 100.00%	0.00% / 0.00% / 0.61%
Books	user_split	94,864 / 100.00% / 89.17%	94,864 / 100.00% / 89.17%	31,567 / 100.00% / 89.06%	32,453 / 100.00% / 89.01%	0.00% / 0.01% / 0.16%
Books	item_split	94,864 / 100.00% / 89.17%	80,633 / 100.00% / 89.14%	4,744 / 100.00% / 88.47%	9,487 / 100.00% / 89.73%	0.00% / 0.01% / 0.16%
Books	leave_last_out	94,864 / 100.00% / 89.17%	94,829 / 100.00% / 89.17%	80,327 / 100.00% / 89.17%	76,778 / 100.00% / 89.13%	0.00% / 0.01% / 0.14%
Books	temporal	96,465 / 100.00% / 88.36%	75,631 / 100.00% / 88.73%	37,923 / 100.00% / 88.00%	26,194 / 100.00% / 87.40%	0.00% / 0.02% / 0.18%
CDs_and_Vinyl	user_split	18,586 / 100.00% / 99.90%	18,586 / 100.00% / 99.90%	8,295 / 100.00% / 99.89%	8,493 / 100.00% / 99.89%	0.00% / 0.00% / 6.42%
CDs_and_Vinyl	item_split	18,586 / 100.00% / 99.90%	15,797 / 100.00% / 99.91%	930 / 100.00% / 99.68%	1,859 / 100.00% / 100.00%	0.00% / 0.00% / 6.44%
CDs_and_Vinyl	leave_last_out	18,586 / 100.00% / 99.90%	18,584 / 100.00% / 99.90%	16,271 / 100.00% / 99.90%	16,039 / 100.00% / 99.89%	0.00% / 0.00% / 6.44%
CDs_and_Vinyl	temporal	13,650 / 100.00% / 99.88%	11,048 / 100.00% / 99.86%	6,986 / 100.00% / 99.86%	4,566 / 100.00% / 99.82%	0.00% / 0.00% / 6.61%
Cell_Phones_and_Accessories	user_split	19,322 / 100.00% / 100.00%	19,322 / 100.00% / 100.00%	6,692 / 100.00% / 100.00%	6,736 / 100.00% / 100.00%	0.00% / 0.01% / 0.50%
Cell_Phones_and_Accessories	item_split	19,322 / 100.00% / 100.00%	16,422 / 100.00% / 100.00%	967 / 100.00% / 100.00%	1,933 / 100.00% / 100.00%	0.00% / 0.01% / 0.51%
Cell_Phones_and_Accessories	leave_last_out	19,322 / 100.00% / 100.00%	19,315 / 100.00% / 100.00%	16,504 / 100.00% / 100.00%	15,069 / 100.00% / 100.00%	0.00% / 0.01% / 0.58%
Cell_Phones_and_Accessories	temporal	19,096 / 100.00% / 100.00%	10,107 / 100.00% / 100.00%	7,932 / 100.00% / 100.00%	10,338 / 100.00% / 100.00%	0.00% / 0.00% / 0.34%
Clothing_Shoes_and_Jewelry	user_split	14,289 / 100.00% / 99.98%	14,289 / 100.00% / 99.98%	8,145 / 100.00% / 100.00%	8,166 / 100.00% / 99.98%	0.00% / 0.00% / 0.08%
Clothing_Shoes_and_Jewelry	item_split	14,289 / 100.00% / 99.98%	12,145 / 100.00% / 99.98%	715 / 100.00% / 100.00%	1,429 / 100.00% / 100.00%	0.00% / 0.00% / 0.07%
Clothing_Shoes_and_Jewelry	leave_last_out	14,289 / 100.00% / 99.98%	14,289 / 100.00% / 99.98%	13,220 / 100.00% / 99.98%	12,877 / 100.00% / 99.98%	0.00% / 0.00% / 0.09%
Clothing_Shoes_and_Jewelry	temporal	24,824 / 100.00% / 99.99%	10,351 / 100.00% / 99.97%	20,663 / 100.00% / >99.99%	19,421 / 100.00% / >99.99%	0.00% / 0.00% / 0.10%
Digital_Music	user_split	2,341 / 100.00% / 100.00%	2,341 / 100.00% / 100.00%	252 / 100.00% / 100.00%	240 / 100.00% / 100.00%	0.00% / 3.52% / 36.12%
Digital_Music	item_split	2,389 / 100.00% / 100.00%	2,030 / 100.00% / 100.00%	119 / 100.00% / 100.00%	236 / 100.00% / 100.00%	0.00% / 3.58% / 36.70%
Digital_Music	leave_last_out	16,240 / 100.00% / 100.00%	12,553 / 100.00% / 100.00%	2,184 / 100.00% / 100.00%	2,177 / 100.00% / 100.00%	0.00% / 10.79% / 62.42%
Digital_Music	temporal	8,154 / 100.00% / 100.00%	4,145 / 100.00% / 100.00%	1,121 / 100.00% / 100.00%	735 / 100.00% / 100.00%	0.00% / 8.47% / 61.69%
Electronics	user_split	92,180 / 100.00% / >99.99%	92,180 / 100.00% / >99.99%	31,288 / 100.00% / >99.99%	31,489 / 100.00% / >99.99%	0.00% / 0.06% / 0.71%
Electronics	item_split	92,180 / 100.00% / >99.99%	78,353 / 100.00% / >99.99%	4,609 / 100.00% / 100.00%	9,218 / 100.00% / 100.00%	0.00% / 0.06% / 0.74%
Electronics	leave_last_out	92,180 / 100.00% / >99.99%	92,177 / 100.00% / >99.99%	72,119 / 100.00% / >99.99%	66,509 / 100.00% / >99.99%	0.00% / 0.06% / 0.76%
Electronics	temporal	78,239 / 100.00% / >99.99%	58,356 / 100.00% / >99.99%	39,800 / 100.00% / >99.99%	40,647 / 100.00% / 100.00%	0.00% / 0.06% / 0.61%
Gift_Cards	user_split	717 / 100.00% / 100.00%	717 / 100.00% / 100.00%	230 / 100.00% / 100.00%	240 / 100.00% / 100.00%	0.00% / <0.01% / 0.74%
Gift_Cards	item_split	575 / 100.00% / 100.00%	488 / 100.00% / 100.00%	29 / 100.00% / 100.00%	58 / 100.00% / 100.00%	0.00% / <0.01% / 0.80%
Gift_Cards	leave_last_out	503 / 100.00% / 100.00%	439 / 100.00% / 100.00%	253 / 100.00% / 100.00%	244 / 100.00% / 100.00%	0.00% / 0.00% / 0.88%
Gift_Cards	temporal	421 / 100.00% / 100.00%	278 / 100.00% / 100.00%	123 / 100.00% / 100.00%	120 / 100.00% / 100.00%	0.00% / 0.00% / 0.88%
Grocery_and_Gourmet_Food	user_split	16,735 / 100.00% / 99.94%	16,735 / 100.00% / 99.94%	7,768 / 100.00% / 99.96%	7,813 / 100.00% / 99.95%	0.00% / 0.01% / 0.90%
Grocery_and_Gourmet_Food	item_split	16,735 / 100.00% / 99.94%	14,224 / 100.00% / 99.94%	837 / 100.00% / 100.00%	1,674 / 100.00% / 99.94%	0.00% / <0.01% / 0.87%
Grocery_and_Gourmet_Food	leave_last_out	16,735 / 100.00% / 99.94%	16,735 / 100.00% / 99.94%	15,347 / 100.00% / 99.93%	14,851 / 100.00% / 99.94%	0.00% / 0.01% / 0.92%
Grocery_and_Gourmet_Food	temporal	19,238 / 100.00% / 99.96%	12,329 / 100.00% / 99.97%	14,478 / 100.00% / 99.97%	13,429 / 100.00% / 99.94%	0.00% / 0.02% / 0.95%
Handmade_Products	user_split	11,613 / 100.00% / 100.00%	11,613 / 100.00% / 100.00%	1,725 / 100.00% / 100.00%	1,788 / 100.00% / 100.00%	0.00% / 0.00% / 0.95%
Handmade_Products	item_split	11,878 / 100.00% / 100.00%	10,096 / 100.00% / 100.00%	587 / 100.00% / 100.00%	1,180 / 100.00% / 100.00%	0.00% / 0.00% / 0.94%
Handmade_Products	leave_last_out	14,723 / 100.00% / 100.00%	9,726 / 100.00% / 100.00%	3,350 / 100.00% / 100.00%	3,347 / 100.00% / 100.00%	0.00% / 0.00% / 1.81%
Handmade_Products	temporal	11,710 / 100.00% / 100.00%	5,138 / 100.00% / 100.00%	2,029 / 100.00% / 100.00%	1,708 / 100.00% / 100.00%	0.00% / 0.00% / 1.77%
Health_and_Household	user_split	19,922 / 100.00% / 100.00%	19,922 / 100.00% / 100.00%	9,180 / 100.00% / 100.00%	8,986 / 100.00% / 100.00%	0.00% / 0.00% / 0.13%
Health_and_Household	item_split	19,922 / 100.00% / 100.00%	16,932 / 100.00% / 100.00%	997 / 100.00% / 100.00%	1,993 / 100.00% / 100.00%	0.00% / 0.00% / 0.15%
Health_and_Household	leave_last_out	19,922 / 100.00% / 100.00%	19,922 / 100.00% / 100.00%	16,439 / 100.00% / 100.00%	15,703 / 100.00% / 100.00%	0.00% / 0.00% / 0.13%
Health_and_Household	temporal	19,907 / 100.00% / 100.00%	12,277 / 100.00% / 100.00%	13,166 / 100.00% / 100.00%	14,545 / 100.00% / 100.00%	0.00% / 0.00% / 0.14%
Health_and_Personal_Care	user_split	15,041 / 100.00% / 100.00%	15,041 / 100.00% / 100.00%	940 / 100.00% / 100.00%	877 / 100.00% / 100.00%	0.00% / 1.65% / 34.11%
Health_and_Personal_Care	item_split	17,288 / 100.00% / 100.00%	14,694 / 100.00% / 100.00%	784 / 100.00% / 100.00%	1,585 / 100.00% / 100.00%	0.00% / 1.58% / 34.09%
Health_and_Personal_Care	leave_last_out	3,448 / 100.00% / 100.00%	2,697 / 100.00% / 100.00%	911 / 100.00% / 100.00%	857 / 100.00% / 100.00%	0.00% / 0.99% / 30.43%
Health_and_Personal_Care	temporal	7,604 / 100.00% / 100.00%	4,625 / 100.00% / 100.00%	1,603 / 100.00% / 100.00%	762 / 100.00% / 100.00%	0.00% / 1.16% / 32.96%
Home_and_Kitchen	user_split	17,431 / 100.00% / 100.00%	17,431 / 100.00% / 100.00%	9,721 / 100.00% / 100.00%	9,750 / 100.00% / 100.00%	0.00% / 0.00% / 0.10%
Home_and_Kitchen	item_split	17,431 / 100.00% / 100.00%	14,815 / 100.00% / 100.00%	872 / 100.00% / 100.00%	1,744 / 100.00% / 100.00%	0.00% / 0.00% / 0.10%
Home_and_Kitchen	leave_last_out	17,431 / 100.00% / 100.00%	17,431 / 100.00% / 100.00%	15,181 / 100.00% / 100.00%	14,374 / 100.00% / 100.00%	0.00% / 0.00% / 0.11%
Home_and_Kitchen	temporal	14,879 / 100.00% / 100.00%	11,240 / 100.00% / 100.00%	11,264 / 100.00% / 100.00%	10,726 / 100.00% / 100.00%	0.00% / 0.00% / 0.07%
Industrial_and_Scientific	user_split	19,251 / 100.00% / 100.00%	19,251 / 100.00% / 100.00%	5,348 / 100.00% / 100.00%	5,350 / 100.00% / 100.00%	0.00% / <0.01% / 0.45%
Industrial_and_Scientific	item_split	19,251 / 100.00% / 100.00%	16,362 / 100.00% / 100.00%	963 / 100.00% / 100.00%	1,926 / 100.00% / 100.00%	0.00% / 0.01% / 0.46%
Industrial_and_Scientific	leave_last_out	19,251 / 100.00% / 100.00%	19,229 / 100.00% / 100.00%	16,742 / 100.00% / 100.00%	16,000 / 100.00% / 100.00%	0.00% / 0.01% / 0.50%
Industrial_and_Scientific	temporal	17,690 / 100.00% / 100.00%	10,270 / 100.00% / 100.00%	10,279 / 100.00% / 100.00%	10,034 / 100.00% / 100.00%	0.00% / 0.01% / 0.36%
Kindle_Store	user_split	17,800 / 100.00% / 89.30%	17,800 / 100.00% / 89.30%	13,209 / 100.00% / 89.36%	13,406 / 100.00% / 89.39%	0.00% / 0.00% / 0.02%
Kindle_Store	item_split	17,800 / 100.00% / 89.30%	15,130 / 100.00% / 89.25%	890 / 100.00% / 90.67%	1,780 / 100.00% / 89.10%	0.00% / 0.00% / 0.02%
Kindle_Store	leave_last_out	17,800 / 100.00% / 89.30%	17,800 / 100.00% / 89.30%	15,961 / 100.00% / 89.34%	15,154 / 100.00% / 89.34%	0.00% / 0.00% / 0.02%
Kindle_Store	temporal	19,957 / 100.00% / 89.28%	16,217 / 100.00% / 89.28%	15,954 / 100.00% / 89.31%	14,155 / 100.00% / 89.21%	0.00% / 0.00% / 0.02%
Magazine_Subscriptions	user_split	1,708 / 100.00% / 100.00%	1,708 / 100.00% / 100.00%	472 / 100.00% / 100.00%	463 / 100.00% / 100.00%	0.00% / 4.54% / 61.56%
Magazine_Subscriptions	item_split	1,241 / 100.00% / 100.00%	1,053 / 100.00% / 100.00%	63 / 100.00% / 100.00%	124 / 100.00% / 100.00%	0.00% / 5.08% / 61.97%
Magazine_Subscriptions	leave_last_out	1,110 / 100.00% / 100.00%	918 / 100.00% / 100.00%	353 / 100.00% / 100.00%	381 / 100.00% / 100.00%	0.00% / 4.57% / 62.26%
Magazine_Subscriptions	temporal	522 / 100.00% / 100.00%	329 / 100.00% / 100.00%	117 / 100.00% / 100.00%	66 / 100.00% / 100.00%	0.00% / 4.34% / 59.04%
Movies_and_TV	user_split	18,324 / 100.00% / 100.00%	18,324 / 100.00% / 100.00%	10,254 / 100.00% / 100.00%	10,070 / 100.00% / 100.00%	0.00% / 0.44% / 26.86%
Movies_and_TV	item_split	18,324 / 100.00% / 100.00%	15,574 / 100.00% / 100.00%	917 / 100.00% / 100.00%	1,833 / 100.00% / 100.00%	0.00% / 0.34% / 26.72%
Movies_and_TV	leave_last_out	18,324 / 100.00% / 100.00%	18,324 / 100.00% / 100.00%	16,454 / 100.00% / 100.00%	15,927 / 100.00% / 100.00%	0.00% / 0.45% / 26.62%
Movies_and_TV	temporal	17,522 / 100.00% / 100.00%	14,804 / 100.00% / 100.00%	10,612 / 100.00% / 100.00%	6,941 / 100.00% / 100.00%	0.00% / 0.52% / 36.33%
Musical_Instruments	user_split	16,766 / 100.00% / 99.98%	16,766 / 100.00% / 99.98%	5,933 / 100.00% / 99.97%	5,863 / 100.00% / 99.98%	0.00% / 0.02% / 0.73%
Musical_Instruments	item_split	16,766 / 100.00% / 99.98%	14,250 / 100.00% / 99.97%	839 / 100.00% / 100.00%	1,677 / 100.00% / 100.00%	0.00% / 0.02% / 0.71%
Musical_Instruments	leave_last_out	16,766 / 100.00% / 99.98%	16,760 / 100.00% / 99.98%	12,985 / 100.00% / 99.97%	12,681 / 100.00% / 99.98%	0.00% / 0.02% / 0.78%
Musical_Instruments	temporal	15,914 / 100.00% / 99.98%	11,545 / 100.00% / >99.99%	10,123 / 100.00% / >99.99%	9,093 / 100.00% / 99.98%	0.00% / 0.01% / 0.62%
Office_Products	user_split	12,740 / 100.00% / 99.69%	12,740 / 100.00% / 99.69%	5,177 / 100.00% / 99.77%	5,178 / 100.00% / 99.69%	0.00% / 0.00% / 0.21%
Office_Products	item_split	12,740 / 100.00% / 99.69%	10,829 / 100.00% / 99.70%	637 / 100.00% / 99.69%	1,274 / 100.00% / 99.69%	0.00% / 0.00% / 0.23%
Office_Products	leave_last_out	12,740 / 100.00% / 99.69%	12,738 / 100.00% / 99.69%	12,194 / 100.00% / 99.69%	11,905 / 100.00% / 99.69%	0.00% / 0.00% / 0.21%
Office_Products	temporal	19,965 / 100.00% / 99.70%	12,805 / 100.00% / 99.67%	11,142 / 100.00% / 99.69%	12,535 / 100.00% / 99.73%	0.00% / <0.01% / 0.26%
Patio_Lawn_and_Garden	user_split	14,019 / 100.00% / 99.96%	14,019 / 100.00% / 99.96%	6,556 / 100.00% / 99.95%	6,539 / 100.00% / 99.94%	0.00% / 0.00% / 0.13%
Patio_Lawn_and_Garden	item_split	14,019 / 100.00% / 99.96%	11,916 / 100.00% / 99.96%	701 / 100.00% / 100.00%	1,402 / 100.00% / 100.00%	0.00% / 0.00% / 0.14%
Patio_Lawn_and_Garden	leave_last_out	14,019 / 100.00% / 99.96%	14,019 / 100.00% / 99.96%	13,333 / 100.00% / 99.96%	12,826 / 100.00% / 99.96%	0.00% / 0.00% / 0.14%
Patio_Lawn_and_Garden	temporal	14,329 / 100.00% / 99.97%	8,247 / 100.00% / 99.95%	10,667 / 100.00% / 99.95%	9,718 / 100.00% / 99.95%	0.00% / 0.00% / 0.12%
Pet_Supplies	user_split	19,880 / 100.00% / 99.98%	19,880 / 100.00% / 99.98%	8,436 / 100.00% / 99.98%	8,533 / 100.00% / 100.00%	0.00% / <0.01% / 0.21%
Pet_Supplies	item_split	19,880 / 100.00% / 99.98%	16,898 / 100.00% / 99.98%	994 / 100.00% / 100.00%	1,988 / 100.00% / 100.00%	0.00% / <0.01% / 0.21%
Pet_Supplies	leave_last_out	19,880 / 100.00% / 99.98%	19,880 / 100.00% / 99.98%	16,106 / 100.00% / 99.98%	15,361 / 100.00% / 99.98%	0.00% / <0.01% / 0.22%
Pet_Supplies	temporal	18,400 / 100.00% / 99.98%	13,065 / 100.00% / 99.98%	11,921 / 100.00% / 99.97%	12,385 / 100.00% / 99.98%	0.00% / <0.01% / 0.15%
Software	user_split	13,664 / 100.00% / 100.00%	13,664 / 100.00% / 100.00%	4,254 / 100.00% / 100.00%	4,129 / 100.00% / 100.00%	0.00% / 0.00% / 0.43%
Software	item_split	13,664 / 100.00% / 100.00%	11,613 / 100.00% / 100.00%	684 / 100.00% / 100.00%	1,367 / 100.00% / 100.00%	0.00% / 0.00% / 0.39%
Software	leave_last_out	13,664 / 100.00% / 100.00%	13,639 / 100.00% / 100.00%	9,800 / 100.00% / 100.00%	9,400 / 100.00% / 100.00%	0.00% / 0.00% / 0.42%
Software	temporal	14,548 / 100.00% / >99.99%	11,639 / 100.00% / 100.00%	5,902 / 100.00% / 100.00%	3,762 / 100.00% / 100.00%	0.00% / <0.01% / 0.71%
Sports_and_Outdoors	user_split	19,023 / 100.00% / 100.00%	19,023 / 100.00% / 100.00%	7,398 / 100.00% / 100.00%	7,431 / 100.00% / 100.00%	0.00% / 0.01% / 0.27%
Sports_and_Outdoors	item_split	19,023 / 100.00% / 100.00%	16,168 / 100.00% / 100.00%	952 / 100.00% / 100.00%	1,903 / 100.00% / 100.00%	0.00% / 0.01% / 0.29%
Sports_and_Outdoors	leave_last_out	19,023 / 100.00% / 100.00%	19,023 / 100.00% / 100.00%	17,601 / 100.00% / 100.00%	16,816 / 100.00% / 100.00%	0.00% / 0.01% / 0.30%
Sports_and_Outdoors	temporal	18,592 / 100.00% / 100.00%	11,498 / 100.00% / 100.00%	10,372 / 100.00% / 100.00%	11,653 / 100.00% / 100.00%	0.00% / 0.00% / 0.20%
Subscription_Boxes	user_split	291 / 99.66% / 100.00%	291 / 99.66% / 100.00%	28 / 100.00% / 100.00%	36 / 97.22% / 100.00%	0.19% / 0.19% / 6.12%
Subscription_Boxes	item_split	327 / 99.69% / 100.00%	277 / 99.64% / 100.00%	16 / 100.00% / 100.00%	31 / 100.00% / 100.00%	0.29% / 0.29% / 7.76%
Subscription_Boxes	leave_last_out	86 / 100.00% / 100.00%	52 / 100.00% / 100.00%	23 / 100.00% / 100.00%	21 / 100.00% / 100.00%	0.00% / 0.00% / 8.82%
Subscription_Boxes	temporal	191 / 100.00% / 100.00%	86 / 100.00% / 100.00%	62 / 100.00% / 100.00%	34 / 100.00% / 100.00%	0.00% / 0.00% / 4.51%
Tools_and_Home_Improvement	user_split	17,307 / 100.00% / >99.99%	17,307 / 100.00% / >99.99%	8,377 / 100.00% / 100.00%	8,199 / 100.00% / 100.00%	0.00% / 0.00% / 0.14%
Tools_and_Home_Improvement	item_split	17,307 / 100.00% / >99.99%	14,710 / 100.00% / >99.99%	866 / 100.00% / 100.00%	1,731 / 100.00% / 100.00%	0.00% / 0.00% / 0.11%
Tools_and_Home_Improvement	leave_last_out	17,307 / 100.00% / >99.99%	17,307 / 100.00% / >99.99%	15,754 / 100.00% / 100.00%	15,151 / 100.00% / 100.00%	0.00% / 0.00% / 0.15%
Tools_and_Home_Improvement	temporal	15,514 / 100.00% / 100.00%	11,124 / 100.00% / 100.00%	11,518 / 100.00% / 100.00%	11,481 / 100.00% / 100.00%	0.00% / 0.00% / 0.11%
Toys_and_Games	user_split	15,609 / 100.00% / >99.99%	15,609 / 100.00% / >99.99%	7,090 / 100.00% / 99.99%	7,031 / 100.00% / 100.00%	0.00% / 0.00% / 0.09%
Toys_and_Games	item_split	15,609 / 100.00% / >99.99%	13,267 / 100.00% / >99.99%	781 / 100.00% / 100.00%	1,561 / 100.00% / 100.00%	0.00% / 0.00% / 0.10%
Toys_and_Games	leave_last_out	15,609 / 100.00% / >99.99%	15,608 / 100.00% / >99.99%	14,818 / 100.00% / >99.99%	14,342 / 100.00% / >99.99%	0.00% / 0.00% / 0.10%
Toys_and_Games	temporal	17,369 / 100.00% / 99.99%	10,382 / 100.00% / >99.99%	8,553 / 100.00% / 99.98%	11,428 / 100.00% / >99.99%	0.00% / 0.00% / 0.05%
Video_Games	user_split	19,227 / 100.00% / >99.99%	19,227 / 100.00% / >99.99%	5,574 / 100.00% / 100.00%	5,551 / 100.00% / 100.00%	0.00% / 0.07% / 1.63%
Video_Games	item_split	19,227 / 100.00% / >99.99%	16,342 / 100.00% / >99.99%	962 / 100.00% / 100.00%	1,923 / 100.00% / 100.00%	0.00% / 0.08% / 1.72%
Video_Games	leave_last_out	19,227 / 100.00% / >99.99%	19,202 / 100.00% / >99.99%	15,526 / 100.00% / >99.99%	15,042 / 100.00% / 100.00%	0.00% / 0.07% / 1.68%
Video_Games	temporal	16,463 / 100.00% / >99.99%	11,338 / 100.00% / >99.99%	7,650 / 100.00% / 99.99%	7,253 / 100.00% / 100.00%	0.00% / 0.08% / 1.64%

Reproducing the audit

examples/validation/amazon_metadata_audit.py catalogs rebuilds the selected split payloads from sealed prepared inputs using the original frozen profiler/builder and checks their measured counts. It saves retained item IDs and training item frequencies, not reusable checkpoints. Its scan mode streams the cached metadata in bounded memory, deduplicates retained item IDs using the adapter’s first-record policy, and measures exactly those catalogs. It does not download missing sources; a missing retained metadata record fails the audit. Use separate output directories and keep the frozen audit source unchanged. The published archive retains the original pre-change formatter fingerprints. New scans also fingerprint the live adapter and base formatter, preventing a resume from silently combining results produced by different implementations.