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# Reliability calibration

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  1. Random splits flattered machine-learned steel column corrections; holding out whole test programmes drops the safety factor to 4.01–8.38

    Machine-learned corrections to global buckling predictions for built-up cold-formed steel columns were previously judged on a random 80/20 split, where specimens from one test programme can sit in both training and test sets. Holding out entire experimental sources instead, across 148 tests from 15 sources and 13 section families, weakens transfer sharply: the best model, Lasso, reaches an R2R^2 of 0.787 and a mean absolute percentage error of 73.0%. Calibrated on those out-of-source predictions, LRFD resistance factors fall to 0.191–0.399 and ASD safety factors to 4.01–8.38.

  2. medRxiv

    A protective effect in observational data may be all bias: new metric attributes a 16.9% mortality drop to systematic error

    Observational causal estimates gain a continuous reporting channel: the Bias Attribution Fraction (BAF) writes the share of an observed log relative risk attributable to systematic bias as a number between 0 and 1, with a 95% credible interval. Across 1,920,000 simulated repetitions, the calibrated P-value alone declared 36.02% of truly bias-dominated repetitions usable, adding the BAF point estimate cut that to 16.48%, and requiring the whole credible interval below 0.5 cut it to 10.16%. In a MIMIC IV beta-blocker target trial emulation, the 16.9% one-year mortality reduction associated with reaching target dose disappeared after calibration, with BAF 0.875 and a verdict of not usable as effect evidence.

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