# Correction Logs — String Normalisation + Secondary-Measure Weighted Mean

> 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).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-news-04-034?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-11-02
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/correction-logs-string-normalisation-secondary-measure-weighted-mean-277bee56
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_0ZXEOxOmFn4svVd9ZPcdu

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability correction-logs-string-normalisation-secondary-measure-weighted-mean-277bee56 -d '<json body>'
```

Example prompt: I have correction log records with channel_group, date, correction_count, and published_items columns — can you run string normalization on the channel labels and compute a weighted mean for correction_count using published_items as the secondary measure? Here are my rows: [{"row_id":"row-1","channel_group":"group-a","date":"2024-01-15","correction_count":10,"published_items":500},{"row_id":"row-2","channel_group":"group-b","date":"2024-01-16","correction_count":25,"published_items":800}].

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "channel_group",
     "date",
     "correction_count",
     "published_items"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "channel_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "published_items": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "correction_count": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "channel_group": "group-a",
     "published_items": 1,
     "correction_count": 10
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "channel_group": "group-b",
     "published_items": 2,
     "correction_count": 20
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "channel_group": "group-a",
     "published_items": 3,
     "correction_count": 15
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "channel_group": "group-b",
     "published_items": 4,
     "correction_count": 30
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "correction_count",
    "published_items"
   ],
   "type": "string",
   "default": "correction_count"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/correction-logs-string-normalisation-secondary-measure-weighted-mean-277bee56/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)