{"uid":"cap_ACoM-O2ohgoxLcwDUQpGC","slug":"air-quality-observations-iqr-outlier-flags-e6545004","name":"Air Quality Observations – IQR Outlier Flags","description":"Air quality observations - IQR outlier flags: IQR outlier flags for Air quality observations. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded iqr outlier flags report.","url":"https://market.datapackvibe.com/x402/assistant-ar-analytics-air-quality-observations-outliers?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station","date","pm25","humidity_percent"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"pm25":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"station":{"type":"string","default":"","maxLength":80},"humidity_percent":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","pm25":10,"row_id":"row-1","station":"group-a","humidity_percent":1},{"date":"2026-10-02","pm25":20,"row_id":"row-2","station":"group-b","humidity_percent":2},{"date":"2026-10-03","pm25":15,"row_id":"row-3","station":"group-a","humidity_percent":3},{"date":"2026-10-04","pm25":30,"row_id":"row-4","station":"group-b","humidity_percent":4}],"maxItems":200,"minItems":1},"field":{"enum":["pm25","humidity_percent"],"type":"string","default":"pm25"}}},"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_2Dh1pZDySpECGWHly9pz-","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":"Applies interquartile range (IQR) outlier detection to a batch of air quality observation records and returns a flag for each row indicating whether it is an outlier.","exampleAgentPrompt":"Can you run IQR outlier detection on these air quality readings and flag which ones are anomalous? I have up to 50 rows with station name, date, PM2.5, and humidity — flag outliers on the pm25 field.","exampleUseCases":[{"title":"Detecting rogue sensor spikes in PM2.5 data","prompt":"I have a week of PM2.5 readings from 30 air quality stations and I think a few sensors are misbehaving. Can you run IQR outlier flagging on the pm25 field across all rows and tell me which readings are flagged as outliers?"},{"title":"Humidity anomaly audit before a report","prompt":"Before I submit our monthly environmental report, can you check this batch of humidity_percent readings from our monitoring stations for any statistical outliers using IQR? Here are the 80 rows with station, date, and humidity values."},{"title":"Automated QA pass on ingested sensor data","prompt":"We just ingested a fresh batch of air quality observations from our field stations — can you run an IQR outlier check on the pm25 values for all 150 rows and return which row IDs are flagged so I can quarantine them before analysis?"}],"resultDescription":"Returns a report per submitted row indicating whether each record is flagged as an IQR outlier for the chosen field (pm25 or humidity_percent), based purely on local descriptive statistics computed over the supplied dataset. No external enrichment or forecasting is applied.","failureModes":["Fewer than 1 row submitted — request rejected","More than 200 rows submitted — exceeds batch limit","Missing required fields (row_id, station, date, pm25, humidity_percent) — validation error","Invalid date format (must be YYYY-MM-DD) — schema rejection","Invalid field enum value (must be 'pm25' or 'humidity_percent') — error","All values null for chosen field — IQR computation undefined","Payment not included or insufficient — 402 response"],"whenToPreferThis":"Choose this endpoint when you need fast, local IQR-based outlier detection on a bounded batch (up to 200 rows) of air quality observations without requiring external enrichment, forecasting, or ML inference. It is ideal for data QA pipelines, pre-report validation passes, and sensor-data health checks where a simple statistical flagging approach is sufficient and cost must stay low ($0.01 per call).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:25.084Z","isFirstParty":false,"canonicalSlug":"air-quality-observations-iqr-outlier-flags-e6545004"}