{"uid":"cap_mxL-S2kwZ0XZX6YUieFJ2","slug":"label-review-numeric-interval-filter-explicit-missing-value-fill-94ce1453","name":"Label Review — Numeric Interval Filter + Explicit Missing-Value Fill","description":"Label review — Numeric interval filter + Explicit missing-value fill: Numeric interval filter + Explicit missing-value fill 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-12-135?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_t6S7aclTrtTAZSqOrJkMU","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":"Filters label review records to a numeric interval and fills missing values using zero, mean, or median imputation, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Can you filter my label review records to only keep rows where reviewed_records is between 0 and 500, fill any missing reviewed_records values using the median method, and give me the two analytical reports? Here are up to 200 rows with row_id, dataset_group, date, reviewed_records, and changed_labels.","exampleUseCases":[{"title":"Cleaning annotation batch before model training","prompt":"I have 150 label review rows with some null reviewed_records values and a few outliers. Can you filter reviewed_records to the range 0–1000, fill the missing ones using the mean, and send me the two reports so I can prep the dataset for training?"},{"title":"Auditing changed labels within expected bounds","prompt":"For our weekly label audit, filter changed_labels between 0 and 50 across these 80 review records, fill any nulls with zero, and give me the two analytical reports on what's in that range."},{"title":"Detecting out-of-range reviewed record counts","prompt":"We suspect some reviewed_records entries are way off — can you apply an interval filter from 10 to 200 and use median fill for any missing values in these 120 label review rows, then return both reports?"}],"resultDescription":"Two independent analytical reports derived from the supplied label review rows: one covering the numeric interval-filtered view of the selected field (reviewed_records or changed_labels), and one covering the explicit missing-value fill result. Both reports are computed locally without external lookups or predictions, covering up to 200 input rows.","failureModes":["Fewer than 1 or more than 200 rows supplied — rejected with validation error","minimum value exceeds maximum value — logically invalid interval","Invalid date format (not YYYY-MM-DD) in any row — schema validation failure","row_id missing or empty — required field error","Field enum value not in [reviewed_records, changed_labels] — schema rejection","Method enum not in [zero, mean, median] — schema rejection","All values in selected field are null — mean/median fill may be undefined"],"whenToPreferThis":"Choose this endpoint when you have label review tabular data (reviewed_records, changed_labels) and need both numeric range filtering and missing-value imputation in a single call producing two reports. It is ideal for data cleaning pipelines prior to ML training, annotation audits, or QA workflows where outlier exclusion and null handling are both required. Prefer this over single-operation endpoints when you want both reports bundled for $0.01 without making two separate API calls.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:28.359Z","isFirstParty":false,"canonicalSlug":"label-review-numeric-interval-filter-explicit-missing-value-fill-94ce1453"}