# Field Survey Coverage — String Normalisation + Two-Measure Correlation

> Field Survey Coverage — String Normalisation + 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 2026-11-02).

Normalises string fields and computes two-measure correlation across up to 200 field survey coverage records (surveyed_m2, target_m2), producing two independent analytical reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-weather-22-033?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/field-survey-coverage-string-normalisation-two-measure-correlation-fe58a0aa
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_FJ6lyxkyLSkV9JucxenJG

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 field-survey-coverage-string-normalisation-two-measure-correlation-fe58a0aa -d '<json body>'
```

Example prompt: Can you normalise the station group strings and compute the correlation between surveyed_m2 and target_m2 for my field survey records? I have 4 rows: row-1 on 2026-10-01 for group-a with surveyed 10 m² and target 1 m², row-2 on 2026-10-02 for group-b with 20 m² surveyed and 2 m² target, row-3 on 2026-10-03 for group-a with 15 m² surveyed and 3 m² target, and row-4 on 2026-10-04 for group-b with 30 m² surveyed and 4 m² target.

## When to prefer this

Choose this endpoint when you need both string field normalisation and a correlation analysis between two numeric coverage metrics (surveyed vs target area) on batch survey data up to 200 rows, without requiring any external lookups, predictions, or live data. It is particularly suited for field survey, environmental monitoring, or inspection datasets where station group label inconsistency and coverage-to-target tracking are simultaneous concerns.

## Known failure modes

- More than 200 rows submitted — request rejected or truncated
- Missing required fields (row_id, date, surveyed_m2, target_m2) cause row-level validation errors
- Date strings not matching YYYY-MM-DD pattern rejected
- Null values in both numeric measures may produce undefined correlation
- All rows in a single station_group may limit correlation meaningfulness

## How this service works

Field survey coverage — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied field survey coverage records (surveyed_m2, target_m2). 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 reports: (1) a string normalisation report for the station_group and other string fields, and (2) a two-measure Pearson or equivalent correlation report between surveyed_m2 and target_m2 across all submitted rows. No external data is fetched; all analysis is performed locally on the supplied rows.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "station_group",
     "date",
     "surveyed_m2",
     "target_m2"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "target_m2": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "surveyed_m2": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "station_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "target_m2": 1,
     "surveyed_m2": 10,
     "station_group": "group-a"
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "target_m2": 2,
     "surveyed_m2": 20,
     "station_group": "group-b"
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "target_m2": 3,
     "surveyed_m2": 15,
     "station_group": "group-a"
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "target_m2": 4,
     "surveyed_m2": 30,
     "station_group": "group-b"
    }
   ],
   "maxItems": 200,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/field-survey-coverage-string-normalisation-two-measure-correlation-fe58a0aa/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)