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Correction Logs — String Normalisation + Secondary-Measure Weighted Mean
x402
Correction Logs — 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 2026-11-02).
Normalises string fields and computes a secondary-measure weighted mean over up to 200 correction log records (correction_count, published_items).
Price / call$0.01
Success—
StatusUnknown
How this service works
Correction logs — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean for supplied correction logs records (correction_count, published_items). 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) string-normalised versions of the string fields (e.g. channel_group) across all supplied rows, and (2) a secondary-measure weighted mean for the selected numeric field (correction_count or published_items), computed using the other measure as the weight. Reports are generated locally using shared analytical engines with no external lookups.
Example use cases
Editorial quality review by channel
“I've got 50 correction log rows from our newsroom — each has a channel_group, date, correction_count, and published_items. Can you normalize the channel_group strings and give me a weighted mean of correction_count weighted by published_items so I can compare editorial quality across groups?”
Monthly corrections benchmarking report
“Here are 120 rows of correction log data for October, with row_id, channel_group, date, correction_count, and published_items fields. Please normalize the string fields and compute the secondary-measure weighted mean for published_items so I can benchmark correction rates for this month.”
Audit anomalous correction spikes
“I need to investigate unusually high correction counts in our data — I have 80 correction log records with channel_group labels that may have inconsistent casing. Run string normalization plus a weighted mean on correction_count weighted by published_items so I can spot which groups are outliers.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-news-04-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation and weighted mean analytics on correction log data in a single call, without requiring live data lookup or external enrichment. It is especially useful when channel_group labels may be inconsistently formatted and you need a statistically weighted summary of correction rates relative to publishing volume. Prefer it over generic analytics tools when your dataset is small (≤200 rows) and the specific combination of string cleaning plus weighted mean suffices.
Side effects
- Exceeds 200-row limit — request rejected, must paginate or reduce dataset
- Missing required fields (row_id, channel_group, date, correction_count, published_items) — validation error
- Invalid date format (non-YYYY-MM-DD) — schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — rejected
- Invalid field enum value (must be 'correction_count' or 'published_items') — error response
- Payment of 0.01 USDC not fulfilled via x402 — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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