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Tree Canopy Observations — Record Quality Audit + String Normalisation + Base-100 Series
x402
Tree Canopy Observations — Record Quality Audit + String Normalisation + Base-100 Series 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 a base-100 indexed time series — all computed locally without external lookups.
Price / call$0.01
Success—
StatusUnknown
How this service works
Tree canopy observations — Record quality audit + String normalisation + Base-100 series: Record quality audit + String normalisation + Base-100 series 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
Returns three reports for the supplied canopy observation rows: (1) a record quality audit flagging missing, null, or malformed values; (2) a string normalisation report for the station_group field; and (3) a base-100 indexed numeric series for the chosen field (canopy_m2 or plot_m2) anchored at the specified base row index.
Example use cases
Urban forestry canopy data QA
“I have 120 rows of tree canopy observation records with canopy_m2 and plot_m2 measurements across several station groups — can you run all three reports: record quality audit, string normalisation, and a base-100 series for canopy_m2 starting at index 0, so I can spot bad data before analysis?”
Baseline canopy trend indexing
“Please generate a base-100 indexed series for the plot_m2 field in my canopy dataset starting from row 5, along with a quality audit and string normalisation report — I want to track relative change in plot size across dates.”
Station label consistency check
“My tree canopy dataset has inconsistent station_group labels across 80 rows — can you run the string normalisation and record quality audit reports on it, plus the base-100 series for canopy_m2 from index 0, so I can clean it up before merging with another dataset?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-weather-17-201?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tree canopy observation records (canopy_m2, plot_m2) and need all three of: a data quality audit, string normalisation of categorical labels, and a base-100 time series — in a single call at low cost. It is ideal for pre-analysis data cleaning pipelines where the dataset is up to 200 rows and no external data enrichment or prediction is needed.
Side effects
- More than 200 rows submitted — request will be rejected or truncated
- Missing required fields (row_id, date, canopy_m2, plot_m2) cause row-level errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- base_index out of range (>= number of rows) causes index computation error
- Payment not included or insufficient USDC causes 402 rejection
- All null values in the selected field may produce an empty or degenerate base-100 series
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| base_index | integer |
Tags
x402
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