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Tree Canopy Observations — Quality Audit, String Normalisation & Missing Value Fill
x402
Tree Canopy Observations — Quality Audit, String Normalisation & Missing Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Runs three analytical reports on tree canopy observation records: a record quality audit, string normalisation, and explicit missing-value fill for canopy_m2 and plot_m2 fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Tree canopy observations — Record quality audit + String normalisation + Explicit missing-value fill: Record quality audit + String normalisation + Explicit missing-value fill for supplied tree canopy observations records (canopy_m2, plot_m2). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.
Output
Three independent reports for up to 200 submitted rows: (1) a record quality audit flagging issues per row, (2) a string normalisation report for station_group and other text fields, and (3) a missing-value fill report showing imputed values for the chosen field (canopy_m2 or plot_m2) using the selected method (zero, mean, or median).
Example use cases
Ecological survey data cleanup
“I've got 120 rows of tree canopy survey data with some messy station group names and a bunch of missing canopy_m2 values — can you run a quality audit, normalise the strings, and fill those gaps using the median?”
Plot measurement validation for forestry report
“Before I submit this forestry report, can you check the quality of my 80 plot observation records, fix any inconsistent station group strings, and fill missing plot_m2 values with zero?”
Automated QA pipeline for canopy monitoring
“Every week I get new tree canopy readings — can you audit the record quality, normalise station labels, and impute any missing canopy_m2 entries using the mean so I always have a clean dataset ready?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-17-208?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tree canopy or forestry plot observation records (with canopy_m2 and plot_m2 measurements) that need batch data quality auditing, string normalisation, and missing value imputation in a single cost-effective call. It is well-suited for ecological data pipelines, pre-analysis data cleaning, or before generating reports from survey data. Not appropriate for live data lookup, predictive modelling, or datasets exceeding 200 rows.
Side effects
- Exceeding 200 rows returns a validation error
- Missing required fields (row_id, station_group, date, canopy_m2, plot_m2) causes rejection
- Invalid date format (not YYYY-MM-DD) triggers a schema error
- Numeric values outside allowed range cause rejection
- Invalid enum value for method or field results in a schema error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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