{"uid":"cap_vGDkM12lKKgIri49qqCGr","slug":"tree-canopy-observations-numeric-interval-filter-date-by-category-pivot-202c5120","name":"Tree Canopy Observations — Numeric Interval Filter + Date by Category Pivot","description":"Tree canopy observations — Numeric interval filter + Date by category pivot: Numeric interval filter + Date by category pivot 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-17-133?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_rcbu9yb7O7-Ati3bxo9oG","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":"Filters tree canopy observation rows by a numeric range on canopy_m2 or plot_m2, then produces a date-by-category pivot table — two analytical reports from supplied data, up to 200 rows.","exampleAgentPrompt":"I have 50 tree canopy observation records with row_id, station_group, date, canopy_m2, and plot_m2 columns — can you filter them to only rows where canopy_m2 is between 0 and 500, and also give me a pivot of those observations by date and station group?","exampleUseCases":[{"title":"Urban canopy coverage range audit","prompt":"I have 120 tree plot observations from our city parks survey — each row has a station group, date, canopy_m2, and plot_m2. Filter them to only show rows where canopy_m2 is between 50 and 800, and pivot the results by date and station group so I can see coverage trends."},{"title":"Seasonal monitoring by plot group","prompt":"Here are 80 tree canopy records from our seasonal monitoring across three station groups from 2024. I want to keep only rows where plot_m2 falls between 200 and 5000, and then pivot everything by observation date and station group."},{"title":"Outlier exclusion before reporting","prompt":"I've got 150 canopy measurement rows and I want to exclude anything outside the range of 10 to 1000 for canopy_m2, then show me a date-by-station-group breakdown of what's left so I can prepare a clean report."}],"resultDescription":"Two analytical reports: (1) rows from the supplied dataset filtered to the specified numeric interval on the chosen field (canopy_m2 or plot_m2), and (2) a date-by-category pivot table cross-tabulating values by station_group — both derived entirely from the supplied input rows with no external data lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required row fields (row_id, date, canopy_m2, plot_m2) — validation error","Date values not matching YYYY-MM-DD pattern — schema validation failure","Minimum value greater than maximum value — may yield empty filtered result set","Null numeric values excluded from interval filter computation","Payment of 0.01 USDC not completed — 402 response, no reports returned"],"whenToPreferThis":"Choose this endpoint when you have user-supplied tree canopy observation tabular data (up to 200 rows) and need both a numeric range filter on canopy or plot area and a date-by-station-group pivot in a single call. It is a local analytical engine — no live data lookup, no external APIs, no prediction — making it ideal for deterministic, offline analysis of datasets you already possess. Prefer over general-purpose data tools when cost certainty ($0.01 USDC flat) and the dual-report bundle matter.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T18:51:06.321Z","isFirstParty":false,"canonicalSlug":"tree-canopy-observations-numeric-interval-filter-date-by-category-pivot-202c5120"}