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Label Review Analytics: String Normalisation + Secondary-Measure Weighted Mean
x402
Label Review Analytics: String Normalisation + 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 2026-11-03).
Computes string normalisation and secondary-measure weighted mean analytics on up to 200 label review records (reviewed_records, changed_labels), returning two independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Label review — String normalisation + Secondary-measure weighted mean: String normalisation + 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
Two independent analytical reports: (1) a string normalisation report over the supplied label review records, and (2) a secondary-measure weighted mean report computed across the reviewed_records and changed_labels fields, scoped to up to 200 input rows across specified dataset groups and dates.
Example use cases
ML dataset label audit analytics
“I need to analyse my ML label review batch — I have 5 rows across two dataset groups from last week (2026-10-01 through 2026-10-05) with reviewed_records ranging from 10 to 50 and changed_labels from 1 to 8. Can you run string normalisation and secondary-measure weighted mean on these records using changed_labels as the field?”
Data quality team weekly label report
“Our data quality team reviewed labels across group-a and group-b this month — here are 8 records with dates, reviewed_records counts, and changed_labels counts. Can you generate the string normalisation and weighted mean reports for reviewed_records so we can spot where label changes are concentrated?”
Comparing label stability across dataset groups
“I want to compare label stability between dataset groups — I have 12 label review records from October 2026 covering group-a, group-b, and group-c with their reviewed_records and changed_labels counts. Please compute the string normalisation and secondary-measure weighted mean analytics using changed_labels as the field.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-12-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation and weighted mean analytics on label review data in a single call, without requiring live data lookup, external predictions, or real-time data sources. It is ideal for offline batch analysis of ML labelling pipelines, data quality audits, and label change rate summarisation across dataset groups, particularly when cost efficiency (two reports for $0.01 USDC) and local computation without side effects are priorities.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, reviewed_records, changed_labels) cause validation errors
- Invalid date format (not YYYY-MM-DD) causes schema rejection
- Null values for both reviewed_records and changed_labels may produce undefined weighted mean
- Numbers outside allowed range (±1,000,000,000,000) cause validation failure
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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