{"uid":"cap_8Siwdl-vBOU5dkt9LizRi","slug":"tree-canopy-observations-trailing-mean-series-adjacent-row-changes-15d756a5","name":"Tree Canopy Observations — Trailing Mean Series + Adjacent Row Changes","description":"Tree canopy observations — Trailing mean series + Adjacent row changes: Trailing mean series + Adjacent row changes 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-17-146?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"},"window":{"type":"integer","default":2,"maximum":200,"minimum":2}}},"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_67YvVPYOVFQtIgZu00Maf","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 trailing mean time series and adjacent row changes for user-supplied tree canopy observation records (canopy_m2 or plot_m2), returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have 50 tree canopy observation records — each with a date, station group, canopy_m2, and plot_m2 — and I want to see both a trailing mean series (window of 5) and adjacent row changes for the canopy_m2 field. Can you run those two reports?","exampleUseCases":[{"title":"Urban tree canopy trend analysis","prompt":"I've got 120 rows of tree canopy survey data from our city parks, each with a date, station group, canopy area in square meters, and plot size. Run a trailing mean series with a window of 10 and show me the adjacent row changes for the canopy_m2 field."},{"title":"Ecological plot monitoring over season","prompt":"We recorded canopy and plot measurements at three station groups across 90 dates this growing season. Can you compute a 7-period trailing mean and adjacent change report for the plot_m2 values so we can see growth trends between observations?"},{"title":"Forestry survey QA for consecutive differences","prompt":"I need to audit our forestry plot data for sudden jumps — I have 200 rows with dates, row IDs, station groups, and canopy_m2 measurements. Please generate a trailing mean (window size 4) and an adjacent row change report on canopy_m2 so I can spot anomalies."}],"resultDescription":"Returns two analytical reports: (1) a trailing mean series computed over the selected field (canopy_m2 or plot_m2) using the specified window size, and (2) a set of adjacent row changes showing the difference between consecutive observation records. Results cover up to 200 input rows and are computed entirely from the supplied data.","failureModes":["Fewer than 1 row or more than 200 rows submitted — rejected with validation error","Missing required fields (row_id, station_group, date, canopy_m2, plot_m2) on any row — schema validation failure","Date field does not match YYYY-MM-DD pattern — validation error","Window size smaller than 2 or larger than 200 — rejected","Null values in canopy_m2 or plot_m2 may affect mean/change calculations for affected rows","Invalid field enum value (not 'canopy_m2' or 'plot_m2') — rejected"],"whenToPreferThis":"Choose this endpoint when you have user-supplied tree canopy observation records and need both a rolling/trailing mean time series and adjacent row difference analysis in a single API call at low cost. It is well-suited for ecological, forestry, or urban planning datasets with up to 200 rows where no external data lookup, live sensor integration, or predictive modeling is required — purely local analytical computation on your submitted data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-27T13:06:51.495Z","isFirstParty":false,"canonicalSlug":"tree-canopy-observations-trailing-mean-series-adjacent-row-changes-15d756a5"}