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Tree Canopy Observations — Record Quality Audit + String Normalisation + Keyed Dataset Comparison
x402
Tree Canopy Observations — Record Quality Audit + String Normalisation + Keyed Dataset Comparison 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).
Audits record quality, normalises strings, and compares keyed datasets for up to 200 tree canopy observation records (canopy_m2, plot_m2) in three independent analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Tree canopy observations — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison 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 analytical reports: (1) a record quality audit flagging issues in the supplied canopy observation rows, (2) a string normalisation report for station_group and other string fields, and (3) a keyed dataset comparison report identifying differences between the primary rows and the other dataset matched by row_id. No external lookups or predictions are made; all analysis is performed locally on the supplied data.
Example use cases
Field survey data integrity check
“I've collected 150 tree canopy observations from our field survey with row IDs, station groups, dates, canopy area in m2, and plot area in m2. Can you audit these records for quality issues and normalise the station group strings?”
Comparing baseline vs updated canopy measurements
“I have two versions of our canopy dataset — the original baseline and a revised set with corrections — both keyed by row_id with canopy_m2 and plot_m2 fields. Can you run a keyed comparison to show me exactly what changed between them?”
Pre-analysis data validation for ecological report
“Before I run my ecological analysis, can you check my tree canopy observation records for data quality problems, normalise any inconsistent station group names, and compare them against our reference dataset to flag any discrepancies? Both datasets have up to 80 rows with row_id, station_group, date, canopy_m2, and plot_m2.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-17-207?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need three combined analytical operations — quality audit, string normalisation, and keyed dataset comparison — on tree canopy observation data (canopy_m2, plot_m2) in a single low-cost call. Ideal when you have two related datasets you want to cross-check by row_id without any external API lookups or live data enrichment. Best suited for offline data validation workflows, pre-analysis cleaning steps, or QA pipelines for ecological field data.
Side effects
- Exceeding 200 rows in either array returns a validation error
- Missing required fields (row_id, station_group, date, canopy_m2, plot_m2) causes rejection
- Malformed date strings not matching YYYY-MM-DD pattern fail schema validation
- Numeric values outside the allowed range trigger validation errors
- Empty rows array (minItems:1) returns an error; empty other array is permitted
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| other | array |
Tags
x402
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