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Label Review Analytics — Base-100 Series + Two-Measure Correlation
x402
Label Review Analytics — Base-100 Series + Two-Measure Correlation 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 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.
Price / call$0.01
Success—
StatusUnknown
How this service works
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.
Output
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.
Example use cases
Baseline indexing for annotation sprints
“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?”
QA pipeline correlation audit
“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?”
Label drift monitoring across groups
“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?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-165?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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