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Field Survey Coverage — String Normalisation + Secondary-Measure Weighted Mean
x402
Field Survey Coverage — 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 2026-10-25).
Normalises string fields and computes a secondary-measure weighted mean for up to 200 field survey coverage records (surveyed_m2, target_m2), returning two analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Field survey coverage — String normalisation + Secondary-measure weighted mean: String normalisation + Secondary-measure weighted mean 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 analytical reports per call: (1) a string normalisation report cleaning the station_group and related text fields across all submitted rows, and (2) a secondary-measure weighted mean report computing the weighted average of the selected field (surveyed_m2 or target_m2) across up to 200 rows, using local analytical engines with no external lookups.
Example use cases
Ecology plot coverage summary
“I have 50 ecology survey rows with station groups, dates, and surveyed_m2 and target_m2 values — can you normalise the group name strings and give me a surveyed_m2 weighted mean across all of them?”
Target area compliance check
“Run string normalisation and a target_m2 weighted mean on my field coverage dataset — I have 120 rows with row IDs, dates, station groups, and both surveyed and target square metre readings, and I want to see whether we're hitting our targets on average.”
Multi-station survey aggregation
“I've collected 80 field survey records across three station groups over the past quarter. Can you normalise the station group labels and compute the weighted mean of surveyed_m2 weighted by target_m2 so I can compare coverage performance across groups?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-22-034?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation and a weighted mean statistic on field survey coverage data (surveyed_m2/target_m2) in a single cheap call. It is ideal for small-to-medium datasets (up to 200 rows) where no live data lookup, external enrichment, or prediction is required — purely local analytical processing on your supplied records.
Side effects
- More than 200 rows submitted — request rejected with validation error
- Missing required fields (row_id, date, surveyed_m2, target_m2) — row-level validation failure
- Date field not matching YYYY-MM-DD pattern — schema validation error
- Null values for both measures across all rows — weighted mean undefined or zero
- Numeric values outside ±1 trillion range — rejected by schema bounds
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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