{"uid":"cap_ewjwrULZWtGURC7zs44tV","slug":"label-review-numeric-row-ordering-iqr-outlier-flags-f0a3ecd0","name":"Label Review — Numeric Row Ordering + IQR Outlier Flags","description":"Label review — Numeric row ordering + IQR outlier flags: Numeric row ordering + IQR outlier flags for supplied label review records (reviewed_records, changed_labels). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-12-119?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","reviewed_records","changed_labels"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"changed_labels":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"reviewed_records":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","changed_labels":1,"reviewed_records":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","changed_labels":2,"reviewed_records":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","changed_labels":3,"reviewed_records":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","changed_labels":4,"reviewed_records":30}],"maxItems":200,"minItems":1},"field":{"enum":["reviewed_records","changed_labels"],"type":"string","default":"reviewed_records"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_el1-QRdYE27nanIi1DPR6","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Sorts label review records by numeric value and flags statistical outliers using the interquartile range (IQR) method, producing two independent analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a set of label review records with reviewed_records and changed_labels counts — can you sort them numerically by reviewed_records and flag any IQR outliers? Here are up to 10 rows: row-1 group-a 2026-10-01 reviewed=10 changed=1, row-2 group-b 2026-10-02 reviewed=20 changed=2, etc.","exampleUseCases":[{"title":"Outlier detection in annotation volume","prompt":"I have 50 label review records across two dataset groups from the past month — can you order them by reviewed_records and flag any rows that look like outliers using IQR? I want to spot which annotation sessions were unusually large or small."},{"title":"Ranking changed labels by frequency","prompt":"Can you sort these 30 label review entries by changed_labels count and identify any statistical outliers using IQR? The data has row IDs, dates, and group names — I want to see which sessions had abnormally high label change rates."},{"title":"Data quality audit before model training","prompt":"Before I feed this label review data into training, can you run numeric ordering and IQR outlier detection on the reviewed_records field for these 80 rows? I need to catch any suspicious entries that could skew my model."}],"resultDescription":"Returns two independent reports for the submitted label review rows: (1) a numeric row ordering report that ranks rows by the selected field (reviewed_records or changed_labels), and (2) an IQR outlier detection report that flags rows whose values fall outside the interquartile range bounds. Both reports operate on the same submitted data with no external lookups.","failureModes":["Fewer than 1 row submitted — minimum of 1 row required","More than 200 rows submitted — batch limit exceeded","Missing required fields (row_id, dataset_group, date, reviewed_records, changed_labels) in any row","Invalid date format — must match YYYY-MM-DD pattern","Field enum value not recognized — must be reviewed_records or changed_labels","Null values in the selected numeric field may affect IQR computation","Payment of 0.01 USDC not completed — x402 payment required before processing"],"whenToPreferThis":"Choose this endpoint when you need both a ranked ordering of label review rows and IQR-based outlier detection in a single $0.01 call, without requiring any external data sources or ML predictions. It is ideal for data quality audits of annotation pipelines, pre-training data checks, or monitoring label review session volumes. Prefer this over general-purpose analytics when your data conforms specifically to the reviewed_records/changed_labels schema and you want two distinct statistical reports in one request.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:39:04.751Z","isFirstParty":false,"canonicalSlug":"label-review-numeric-row-ordering-iqr-outlier-flags-f0a3ecd0"}