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Tree Canopy Observations — Record Quality Audit + Adjacent Row Changes
x402
Tree Canopy Observations — Record Quality Audit + Adjacent Row Changes is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-24).
Runs a record quality audit and adjacent row change report on up to 200 tree canopy observation records (canopy_m2, plot_m2) using local analytical engines — no external lookups or predictions.
Price / call$0.01
Success—
StatusUnknown
How this service works
Tree canopy observations — Record quality audit + Adjacent row changes: Record quality audit + Adjacent row changes for supplied tree canopy observations records (canopy_m2, plot_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
Returns two independent reports for the submitted rows: (1) a record quality audit identifying missing values, invalid entries, or format issues in the canopy_m2 or plot_m2 fields; and (2) an adjacent row change report showing value differences between consecutive rows for the selected field, grouped by station and date. All analysis is performed locally with no external data lookups.
Example use cases
Pre-analysis data quality check
“Before I run my forest coverage analysis, can you audit these 150 tree canopy observation records for data quality issues? Check the canopy_m2 field and flag any suspicious row-to-row changes or invalid values across my station groups.”
Survey data validation for ecology team
“My ecology team just submitted their seasonal canopy survey data — can you run a quality audit on these 80 plot records and also show me how canopy_m2 changes between adjacent rows for each station group?”
Detecting measurement errors in monitoring logs
“I think there might be some data entry errors in our canopy monitoring logs. Can you check this batch of 60 records for quality issues and highlight where plot_m2 values jump unexpectedly between consecutive rows?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-17-011?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need simultaneous record quality auditing AND adjacent row change detection on tree canopy observation data (canopy_m2, plot_m2) in a single low-cost call. It is ideal for pre-analysis validation of environmental monitoring datasets up to 200 rows where no external lookup, live data, or professional assessment is needed. Prefer it over generic data validation tools when your data specifically involves canopy area and plot area measurements with station group and date dimensions.
Side effects
- More than 200 rows submitted — request rejected with validation error
- Missing required fields (row_id, date, canopy_m2, plot_m2) — row-level validation failure
- Invalid date format not matching YYYY-MM-DD pattern — schema rejection
- Invalid field selector value not in enum [canopy_m2, plot_m2] — schema error
- Empty rows array — minItems constraint violation
- Payment not included or insufficient — x402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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