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      "label": "t - lookback, t)`, using records\navailable by `t`. Build labels from the outcome window that follows. An unresolved outcome is\nunknown, not a negative. Record `label_available_at` and `outcome_observed` separately from the\nplanned maturity date, because a window can pass while its source ledger is still incomplete. At a\nsimulated training cutoff, use only labels that the observation window, reporting delay and actual\navailability allow. Report missing outcomes by cohort and exclusion reason. Elapsed time alone does\nnot prove follow-up is complete.\n\nTest unique keys, join row counts, timezone conversion, missingness, late arrivals and duplicate\nevents. Store the extraction cutoff and source version with the data. Record permission to use\nthe data, keep customer identifiers out of public examples, and publish only data whose licence and\npermissions allow it. When the original data is private, a reproducible synthetic example is enough.\n\nReproduction needs a data manifest. Record the source URL or export/query version, licence,\nretrieval date, extraction cutoff, raw-file checksum, schema version, code commit and environment.\nWhere permitted, keep raw inputs unchanged. Document any sampling, seeds and exclusions, and use the\nsame population denominator in your text and charts. Run the cleaning and analysis in a fresh\nprocess with the locked environment, not from the notebook's execution history. The questions\nin [Datasheets for Datasets",
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