{"uid":"cap_0ZXEOxOmFn4svVd9ZPcdu","slug":"correction-logs-string-normalisation-secondary-measure-weighted-mean-277bee56","name":"Correction Logs — String Normalisation + Secondary-Measure Weighted Mean","description":"Correction logs — String normalisation + Secondary-measure weighted mean: String normalisation + 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-04-034?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_Lbkc2EAqBaTpGPCkg55Kb","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 secondary-measure weighted mean over up to 200 correction log records (correction_count, published_items).","exampleAgentPrompt":"I have correction log records with channel_group, date, correction_count, and published_items columns — can you run string normalization on the channel labels and compute a weighted mean for correction_count using published_items as the secondary measure? Here are my rows: [{\"row_id\":\"row-1\",\"channel_group\":\"group-a\",\"date\":\"2024-01-15\",\"correction_count\":10,\"published_items\":500},{\"row_id\":\"row-2\",\"channel_group\":\"group-b\",\"date\":\"2024-01-16\",\"correction_count\":25,\"published_items\":800}].","exampleUseCases":[{"title":"Editorial quality review by channel","prompt":"I've got 50 correction log rows from our newsroom — each has a channel_group, date, correction_count, and published_items. Can you normalize the channel_group strings and give me a weighted mean of correction_count weighted by published_items so I can compare editorial quality across groups?"},{"title":"Monthly corrections benchmarking report","prompt":"Here are 120 rows of correction log data for October, with row_id, channel_group, date, correction_count, and published_items fields. Please normalize the string fields and compute the secondary-measure weighted mean for published_items so I can benchmark correction rates for this month."},{"title":"Audit anomalous correction spikes","prompt":"I need to investigate unusually high correction counts in our data — I have 80 correction log records with channel_group labels that may have inconsistent casing. Run string normalization plus a weighted mean on correction_count weighted by published_items so I can spot which groups are outliers."}],"resultDescription":"Returns two analytical reports: (1) string-normalised versions of the string fields (e.g. channel_group) across all supplied rows, and (2) a secondary-measure weighted mean for the selected numeric field (correction_count or published_items), computed using the other measure as the weight. Reports are generated locally using shared analytical engines with no external lookups.","failureModes":["Exceeds 200-row limit — request rejected, must paginate or reduce dataset","Missing required fields (row_id, channel_group, date, correction_count, published_items) — validation error","Invalid date format (non-YYYY-MM-DD) — schema validation failure","Numeric values outside allowed range (±1,000,000,000,000) — rejected","Invalid field enum value (must be 'correction_count' or 'published_items') — error response","Payment of 0.01 USDC not fulfilled via x402 — 402 Payment Required response"],"whenToPreferThis":"Choose this endpoint when you need both string normalisation and weighted mean analytics on correction log data in a single call, without requiring live data lookup or external enrichment. It is especially useful when channel_group labels may be inconsistently formatted and you need a statistically weighted summary of correction rates relative to publishing volume. Prefer it over generic analytics tools when your dataset is small (≤200 rows) and the specific combination of string cleaning plus weighted mean suffices.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T18:38:42.922Z","isFirstParty":false,"canonicalSlug":"correction-logs-string-normalisation-secondary-measure-weighted-mean-277bee56"}