{"uid":"cap_Cbr3E7zQKx3s6lJVwtZJf","slug":"correction-logs-string-normalisation-two-measure-correlation-90316d93","name":"Correction Logs — String Normalisation + Two-Measure Correlation","description":"Correction logs — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation 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-04-033?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}}},"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_Zs6D8TPBosKOTrASp32__","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":"Normalises string fields and computes a two-measure correlation between correction_count and published_items for up to 200 correction log rows, returning two analytical reports.","exampleAgentPrompt":"Can you run string normalisation and a two-measure correlation on these correction logs — I have 4 rows with channel groups 'group-a' and 'group-b', dates from 2026-10-01 to 2026-10-04, correction counts of 10, 20, 15, 30 and published items of 1, 2, 3, 4?","exampleUseCases":[{"title":"News desk correction rate analysis","prompt":"I have a week of correction log data across two channel groups — can you normalise the channel group strings and tell me whether correction_count correlates with published_items? Here are my rows: row-1 on 2026-10-01, group-a, 12 corrections, 100 published; row-2 on 2026-10-02, group-b, 5 corrections, 200 published; row-3 on 2026-10-03, group-a, 20 corrections, 150 published."},{"title":"Publisher quality audit across channels","prompt":"Run a string normalisation and two-measure correlation report on these correction logs for me — I need to check if higher published_items volumes drive more correction_count across our three channel groups over the past month. I'll supply up to 50 rows."},{"title":"Editorial operations data clean-up","prompt":"Our correction log exports have messy channel group labels — can you normalise those strings and also compute the correlation between correction_count and published_items for these 10 rows so we can spot which groups have the worst correction rates?"}],"resultDescription":"Two structured analytical reports: (1) a string normalisation report covering the channel_group and other string fields across up to 200 rows, and (2) a two-measure correlation report quantifying the statistical relationship between correction_count and published_items across the supplied rows.","failureModes":["Missing required fields (row_id, date, channel_group, correction_count, published_items) cause validation errors","Submitting more than 200 rows exceeds the row limit and will be rejected","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Null values for both numeric measures may prevent correlation computation","Payment of 0.01 USDC not provided results in 402 Payment Required response"],"whenToPreferThis":"Choose this endpoint when you have correction log data with both string fields (like channel_group) that need normalisation and two numeric measures (correction_count and published_items) you want correlated, and you want both analyses in a single $0.01 call for up to 200 rows without needing live lookup or prediction.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:38:36.614Z","isFirstParty":false,"canonicalSlug":"correction-logs-string-normalisation-two-measure-correlation-90316d93"}