{"uid":"cap_-H6uJnH4CHSlrosNQT2wX","slug":"correction-logs-adjacent-row-changes-secondary-measure-weighted-mean-60f2cbb5","name":"Correction Logs — Adjacent Row Changes + Secondary-Measure Weighted Mean","description":"Correction logs — Adjacent row changes + Secondary-measure weighted mean: Adjacent row changes + Secondary-measure weighted mean for supplied correction logs records (correction_count, published_items). 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-news-04-159?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","channel_group","date","correction_count","published_items"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"channel_group":{"type":"string","default":"","maxLength":80},"published_items":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"correction_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","channel_group":"group-a","published_items":1,"correction_count":10},{"date":"2026-10-02","row_id":"row-2","channel_group":"group-b","published_items":2,"correction_count":20},{"date":"2026-10-03","row_id":"row-3","channel_group":"group-a","published_items":3,"correction_count":15},{"date":"2026-10-04","row_id":"row-4","channel_group":"group-b","published_items":4,"correction_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["correction_count","published_items"],"type":"string","default":"correction_count"}}},"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_-GZRuNHDs0wIDNu0ZEq_N","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 adjacent row changes and a secondary-measure weighted mean for up to 200 correction log records, returning two analytical reports in a single call.","exampleAgentPrompt":"I have 4 rows of correction log data for my news channels — can you run both the adjacent row changes and the secondary-measure weighted mean analysis on correction_count, using these records: row-1 on 2026-10-01 for group-a with correction_count 10 and published_items 1, row-2 on 2026-10-02 for group-b with correction_count 20 and published_items 2, row-3 on 2026-10-03 for group-a with correction_count 15 and published_items 3, row-4 on 2026-10-04 for group-b with correction_count 30 and published_items 4?","exampleUseCases":[{"title":"News channel correction rate trend","prompt":"I have weekly correction log entries across two channel groups — can you run adjacent row changes and a weighted mean on correction_count for these 6 rows: row-1 through row-6 dated 2026-10-01 to 2026-10-06, alternating group-a and group-b, with correction_counts 5, 12, 8, 20, 6, 15 and published_items 100, 200, 150, 300, 120, 180?"},{"title":"Editorial quality spike detection","prompt":"Can you analyze these correction logs for row-over-row changes and a published_items-weighted mean, using correction_count as the primary field? I've got 5 rows from 2026-09-01 to 2026-09-05, all in channel group 'editorial-team', with correction_counts 3, 7, 2, 14, 5 and published_items 50, 60, 45, 80, 55."},{"title":"Multi-group publishing correction audit","prompt":"Please run the adjacent row changes plus weighted mean reports on my correction log data — I have 8 rows spanning three channel groups (sports, politics, culture) from 2026-11-01 to 2026-11-08 with correction_count values ranging from 2 to 40 and published_items between 10 and 500. I want to analyze the correction_count field."}],"resultDescription":"Returns two analytical reports: (1) adjacent row changes showing how correction_count or published_items values shift between consecutive rows, and (2) a weighted mean of the selected primary field using the secondary measure as weights — both computed locally against the supplied data, with no external lookups or predictions.","failureModes":["Empty or missing rows array returns an error","More than 200 rows submitted exceeds the maxItems limit","Invalid date format (not YYYY-MM-DD) causes schema validation failure","row_id missing or empty string triggers required-field validation error","field enum value outside 'correction_count' or 'published_items' is rejected","Null values in both correction_count and published_items may produce undefined weighted mean","Submitting non-numeric values for numeric fields causes type validation failure"],"whenToPreferThis":"Choose this endpoint when you have tabular correction log data (up to 200 rows) with correction_count and published_items fields and need both row-over-row delta analysis and a weighted mean in a single inexpensive call. It is best suited for editorial quality monitoring, news correction auditing, or publishing analytics pipelines where offline batch computation on supplied data is sufficient and no live data lookup or prediction is required.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T12:48:04.377Z","isFirstParty":false,"canonicalSlug":"correction-logs-adjacent-row-changes-secondary-measure-weighted-mean-60f2cbb5"}