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Label Review Analytics: Adjacent Row Changes + Secondary-Measure Weighted Mean
x402
Label Review Analytics: Adjacent Row Changes + Secondary-Measure Weighted Mean is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Computes adjacent row changes and secondary-measure weighted mean reports for up to 200 label review records in a single paid call.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — Adjacent row changes + Secondary-measure weighted mean: Adjacent row changes + Secondary-measure weighted mean 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.
Output
Returns two analytical reports for the supplied label review rows: (1) an adjacent row changes report showing how reviewed_records or changed_labels shifts between consecutive rows, and (2) a secondary-measure weighted mean report that computes a weighted average using the specified field. Both reports are computed locally against the supplied data with no external lookups.
Example use cases
Annotation QA drift detection
“I have a week of label review logs across two dataset groups — can you check how changed_labels shifts from one row to the next and also give me a weighted mean of reviewed_records weighted by changed_labels? Here are my 5 rows with dates starting 2026-11-01 in groups alpha and beta.”
Daily label correction rate summary
“Run both the adjacent row changes and weighted mean analysis on my daily label review records for October — I've got 20 rows across group-a and group-b with reviewed_records and changed_labels filled in. Use changed_labels as the field for the weighted mean.”
Multi-group annotation audit rollup
“I need to audit our annotation pipeline: please compute the row-to-row changes in changed_labels and the secondary-measure weighted mean for reviewed_records across these 15 label review entries from three dataset groups dated between 2026-09-01 and 2026-09-15.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-159?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both adjacent row change detection and a weighted mean computation on label review data in a single low-cost call. It is purpose-built for annotation QA workflows, dataset audits, and label correction rate analysis where the input is structured review records with reviewed_records and changed_labels columns. Prefer it over generic analytics services when you have up to 200 rows and want two complementary statistical summaries for the price of one call.
Side effects
- More than 200 rows submitted — request exceeds maxItems limit
- Missing required fields (row_id, date, reviewed_records, changed_labels) cause validation errors
- Invalid date format (must be YYYY-MM-DD) triggers schema rejection
- Null values in numeric fields may affect weighted mean computation
- Invalid field enum value (must be reviewed_records or changed_labels) causes rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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