{"uid":"cap_a7h48UuLJdLAxqS8nAsvd","slug":"label-review-analytics-base-100-series-two-measure-correlation-3d54af30","name":"Label Review Analytics — Base-100 Series + Two-Measure Correlation","description":"Label review — Base-100 series + Two-measure correlation: Base-100 series + Two-measure correlation 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-12-165?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"},"base_index":{"type":"integer","default":0,"maximum":199,"minimum":0}}},"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_f21SKuSbciZYw4OCarW9w","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":"Computes a Base-100 index series and a two-measure Pearson/correlation report for label review data (reviewed_records and changed_labels) across up to 200 rows, using local analytical engines with no external lookups.","exampleAgentPrompt":"I have 4 rows of label review data — on 2026-10-01 group-a had 10 reviewed records and 1 changed label, 2026-10-02 group-b had 20 reviewed and 2 changed, 2026-10-03 group-a had 15 reviewed and 3 changed, 2026-10-04 group-b had 30 reviewed and 4 changed — can you run a Base-100 index series using reviewed_records starting from row index 0, and also give me the two-measure correlation between reviewed_records and changed_labels?","exampleUseCases":[{"title":"Baseline indexing for annotation sprints","prompt":"I have weekly label review data across two dataset groups over 8 weeks — up to 50 rows with dates, reviewed_records counts, and changed_labels counts — can you index the reviewed_records values to Base-100 starting from the first row, and also compute the correlation between reviewed_records and changed_labels so I can see how label quality tracks with review volume?"},{"title":"QA pipeline correlation audit","prompt":"My QA team has been logging daily reviewed_records and changed_labels for two dataset groups over the past month — about 60 rows total — can you run both a Base-100 index series on changed_labels starting from row 0 and a two-measure correlation report so I can spot whether high-change-label days are associated with high review volume?"},{"title":"Label drift monitoring across groups","prompt":"I've got 200 rows of label review records split across multiple dataset groups with dates ranging over a quarter — can you normalize the reviewed_records field into a Base-100 series from the beginning and then correlate it against changed_labels to help me understand if label drift is growing relative to review throughput?"}],"resultDescription":"Returns two analytical reports: (1) a Base-100 index series for the chosen field (reviewed_records or changed_labels) anchored at the specified base_index row, showing each row's value normalized relative to that baseline; and (2) a two-measure correlation report quantifying the statistical relationship between reviewed_records and changed_labels across all supplied rows. Both reports are computed locally with no external data enrichment.","failureModes":["Empty or missing rows array returns a validation error","More than 200 rows submitted exceeds the maxItems limit and is rejected","Invalid date format (not YYYY-MM-DD) causes schema validation failure","base_index out of range (>=number of rows) causes computation error","null values in both reviewed_records and changed_labels may produce degenerate correlation output","Non-numeric values in numeric fields are rejected by schema validation","Payment failure returns HTTP 402 and no computation is performed"],"whenToPreferThis":"Choose this endpoint when you have label review tabular data (reviewed_records, changed_labels) and need both a normalized Base-100 trend series and a two-measure correlation in a single low-cost call. It is ideal for ML annotation teams, data QA pipelines, and labeling ops analysts who want local, offline-style analytics without sending data to a prediction service or external enrichment API. It is not appropriate for real-time lookups, forecasting, large datasets exceeding 200 rows, or use cases requiring professional statistical assessment or live data ingestion.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T00:54:31.557Z","isFirstParty":false,"canonicalSlug":"label-review-analytics-base-100-series-two-measure-correlation-3d54af30"}