{"uid":"cap_TdKgtwawraLca84A9GO6l","slug":"tree-canopy-observations-grouped-numeric-summary-missing-value-fill-4d53ee62","name":"Tree Canopy Observations — Grouped Numeric Summary + Missing-Value Fill","description":"Tree canopy observations — Grouped numeric summary + Explicit missing-value fill: Grouped numeric summary + Explicit missing-value fill 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-weather-17-099?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","canopy_m2","plot_m2"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"plot_m2":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"canopy_m2":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","plot_m2":1,"canopy_m2":10,"station_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","plot_m2":2,"canopy_m2":20,"station_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","plot_m2":3,"canopy_m2":15,"station_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","plot_m2":4,"canopy_m2":30,"station_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["canopy_m2","plot_m2"],"type":"string","default":"canopy_m2"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_2cDNzVOeWIlljryjhflIz","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes grouped numeric summaries and fills missing values (canopy_m2, plot_m2) for up to 200 tree canopy observation records using two local analytical reports.","exampleAgentPrompt":"I have 4 tree canopy observation records with some missing canopy_m2 values — can you group them by station_group and get a numeric summary, using median to fill in the missing values?","exampleUseCases":[{"title":"Urban forest coverage analysis by district","prompt":"I have 50 tree canopy measurements from different monitoring stations across the city — can you group them by station_group and summarize the canopy_m2 statistics, using median to fill any missing values?"},{"title":"Plot size data cleaning before modeling","prompt":"Before I run my model I need to fill nulls in my plot_m2 field using zero fill and get a grouped summary — I have about 120 rows with row_id, date, station_group, canopy_m2, and plot_m2."},{"title":"Comparing canopy coverage across research sites","prompt":"Can you aggregate these 80 tree canopy observation records by station group and give me a numeric summary of the canopy_m2 measurements? Use mean imputation for any missing canopy values."}],"resultDescription":"Two analytical reports per call: (1) a grouped numeric summary of the selected field (canopy_m2 or plot_m2) broken down by station_group, and (2) the same records with missing values in the selected field explicitly filled using the chosen method (zero, mean, or median). Results are computed locally without external lookups.","failureModes":["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 pattern validation error","Numeric values outside the allowed range (±1,000,000,000,000) are rejected","Unsupported method or field enum values cause schema validation failure"],"whenToPreferThis":"Choose this endpoint when you have tabular tree canopy or plot observation data that needs grouped aggregation and missing-value imputation in a single call. It is ideal for forestry, environmental, or urban ecology datasets where station-group-level summaries and null handling are needed before downstream analysis, and where no external data lookup or predictive modeling is required.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T18:51:35.392Z","isFirstParty":false,"canonicalSlug":"tree-canopy-observations-grouped-numeric-summary-missing-value-fill-4d53ee62"}