{"uid":"cap_k2eCgwtIAuZYf-6lKhKOY","slug":"tree-canopy-observations-numeric-interval-filter-iqr-outlier-flags-f9062395","name":"Tree Canopy Observations — Numeric Interval Filter + IQR Outlier Flags","description":"Tree canopy observations — Numeric interval filter + IQR outlier flags: Numeric interval filter + IQR outlier flags 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-17-130?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_REkUglC9UASB4vCkTyoxb","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 records by a numeric interval and flags IQR-based outliers across up to 200 rows, returning two analytical reports for canopy_m2 or plot_m2 fields.","exampleAgentPrompt":"I have 150 tree canopy observation records with canopy_m2 and plot_m2 values — can you filter the canopy_m2 field to only keep rows between 0 and 500, and also flag any IQR outliers in those records? Here's the data: [rows array].","exampleUseCases":[{"title":"Urban tree survey quality control","prompt":"I've got survey data from 80 tree monitoring plots across three station groups — can you filter the canopy_m2 values between 10 and 800 and flag any outliers using IQR so I can remove bad readings before analysis?"},{"title":"Plot size anomaly detection for forestry audit","prompt":"Run an interval filter on my plot_m2 field between 50 and 50000, and flag any IQR outliers in these 120 tree canopy observation records so I can identify data entry errors before submitting the audit report."},{"title":"Canopy coverage trend validation","prompt":"Before I compute canopy coverage trends, I need to validate my dataset — filter canopy_m2 records to the range 0 to 1000 and give me IQR outlier flags for these 60 rows grouped by station_group."}],"resultDescription":"Returns two analytical reports for up to 200 supplied rows: (1) a numeric interval filter report listing which records fall within the specified minimum/maximum range for the selected field (canopy_m2 or plot_m2), and (2) an IQR outlier detection report flagging records that fall outside the interquartile range. No external data is fetched; all analysis is performed on the submitted rows.","failureModes":["More than 200 rows submitted — request rejected or truncated","Invalid date format (not YYYY-MM-DD) causes row validation failure","Missing required fields (row_id, canopy_m2, plot_m2) causes schema rejection","Minimum value greater than maximum value produces empty interval filter results","All-null numeric field values may produce undefined IQR statistics","Payment of 0.01 USDC not completed — endpoint returns 402 Payment Required"],"whenToPreferThis":"Choose this endpoint when you have tree canopy observation records (with canopy_m2 and plot_m2 fields) and need both range-based filtering and IQR outlier detection in a single call, without needing live data lookup, predictions, or external enrichment. Ideal for pre-analysis data cleaning, QA pipelines, and audit preparation on small-to-medium datasets (up to 200 rows).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-25T12:33:58.902Z","isFirstParty":false,"canonicalSlug":"tree-canopy-observations-numeric-interval-filter-iqr-outlier-flags-f9062395"}