{"id":"W4387838723","doi":"10.48550/arxiv.2310.12427","title":"Fast Power Curve Approximation for Posterior Analyses","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sample size determination; Posterior probability; Consistency (knowledge bases); Sampling (signal processing); Mathematics; Bayesian probability; Statistics; Bayes' theorem; Power (physics); Sample (material); Sampling distribution; Statistical power; Bayes factor; Statistical hypothesis testing; Computer science; Geometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02055895,0.002168166,0.002438101,0.004014364,0.001085204,0.003811581,0.003714606,0.002593399,0.009875266],"category_scores_gemma":[0.1759389,0.00166489,0.002213733,0.003545917,0.002759143,0.006656806,0.004657313,0.007495553,0.003167435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002406592,"about_ca_system_score_gemma":0.002842685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00386804,"about_ca_topic_score_gemma":0.00226814,"domain_scores_codex":[0.9889395,0.005968585,0.0004791787,0.001265257,0.002961012,0.0003863505],"domain_scores_gemma":[0.9071376,0.0789331,0.002305177,0.006904752,0.004205706,0.0005135388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003040385,0.0000942407,0.002995068,0.0004817976,0.000258366,0.0003530006,0.0005985332,0.3276857,0.002981178,0.4395098,0.006218116,0.2185202],"study_design_scores_gemma":[0.00004251159,0.00004135671,0.0003423418,0.00009327953,0.00003412177,0.0001237117,0.00004735984,0.6735212,0.00145301,0.3188201,0.005457161,0.00002388408],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006656063,0.0001588487,0.9981908,0.00009719592,0.00001618413,0.00003524385,0.00004445648,0.0002082997,0.0005833163],"genre_scores_gemma":[0.08356146,0.0009672253,0.9100669,0.0003308444,0.0001914791,0.0008264332,0.0005357655,0.0008268392,0.002693192],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02055895,"threshold_uncertainty_score":0.1087275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3655998235332305,"score_gpt":0.3443575222995424,"score_spread":0.02124230123368803,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}