{"uid":"cap_KzFknmU0fj8rpkdyX0Ho_","slug":"tree-canopy-observations-numeric-distribution-missing-value-fill-fc08aa9d","name":"Tree Canopy Observations — Numeric Distribution & Missing-Value Fill","description":"Tree canopy observations — Numeric distribution + Explicit missing-value fill: Numeric distribution + 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-17-085?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_pw5B78_sj-ICkQq5PZW5c","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 a numeric distribution summary and fills missing values for tree canopy observation records (canopy_m2, plot_m2) using local analytical engines.","exampleAgentPrompt":"I have up to 200 rows of tree canopy observation records with canopy_m2 and plot_m2 fields — can you run a numeric distribution report and fill any missing canopy_m2 values using the median method across my station groups?","exampleUseCases":[{"title":"Urban tree survey data cleanup","prompt":"I've got a dataset of 150 tree canopy measurements from our city survey — some canopy_m2 values are null. Can you fill those missing values using the median and also give me a numeric distribution report for the canopy_m2 field?"},{"title":"Forest monitoring QA pipeline","prompt":"Before we publish our quarterly forest monitoring results, I need to impute any missing plot_m2 values using zero-fill and get a distribution summary for the plot_m2 field across all 80 station group records."},{"title":"Research dataset normalization","prompt":"I'm preparing tree canopy data for a research paper — I have canopy_m2 and plot_m2 readings from three station groups and about 120 rows. Can you run both a numeric distribution report and a mean imputation for any missing canopy_m2 values?"}],"resultDescription":"Returns two analytical reports for up to 200 supplied rows: (1) a numeric distribution summary (e.g. min, max, mean, percentiles, histogram-style breakdown) for the selected field (canopy_m2 or plot_m2), and (2) a missing-value-filled version of the dataset using the chosen method (zero, mean, or median). No external data is fetched; all analysis runs on the supplied rows.","failureModes":["Empty rows array or more than 200 rows causes a validation error","Invalid date format (not YYYY-MM-DD) triggers a schema rejection","Unrecognized field enum value (anything other than canopy_m2 or plot_m2) causes failure","Unrecognized method enum value (anything other than zero, mean, median) causes failure","Missing required fields (row_id, station_group, date) on any row causes rejection","Payment of 0.01 USDC not provided or x402 auth failure blocks the request"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of tree canopy or plot area observation records and need both a statistical distribution report and missing-value imputation in a single low-cost call. It is ideal for data cleaning and QA pipelines for environmental or urban forestry datasets where you already have the data in hand and do not need external lookups or real-time sensor data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T18:39:13.443Z","isFirstParty":false,"canonicalSlug":"tree-canopy-observations-numeric-distribution-missing-value-fill-fc08aa9d"}