{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003845253,0.0002944763,0.0005018224,0.000219982,0.0001124638,0.00008071352,0.0004362215,0.0002909375,0.0001350902],"category_scores_gemma":[0.001078052,0.0003026293,0.0002758594,0.0003013778,0.00009661441,0.00008785394,0.0004543244,0.0003037143,0.00006579956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001107352,"about_ca_system_score_gemma":0.0000895772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004603545,"about_ca_topic_score_gemma":0.00002435285,"domain_scores_codex":[0.9984311,0.0001660606,0.0002940151,0.0007195656,0.0000836383,0.0003055771],"domain_scores_gemma":[0.9972738,0.001415206,0.0003158557,0.0006280315,0.0002497426,0.0001173617],"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":[0.0001293424,0.0001362199,0.0005979388,0.0008785526,0.0002865304,0.0000778752,0.0002837519,0.0006402722,0.0002164904,0.9934005,0.001407182,0.001945416],"study_design_scores_gemma":[0.0003115335,0.00007032145,0.0007614552,0.0001721078,0.0002896632,0.000001314642,0.000211214,0.05486517,0.0002079564,0.942705,0.0000475394,0.00035672],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04144528,0.000006741977,0.9556983,0.00005988865,0.0004056578,0.0006134643,0.0004231006,0.0002206095,0.001127024],"genre_scores_gemma":[0.6544819,0.0000196265,0.342939,0.00003707786,0.00009186872,0.000009715319,0.00008741084,0.00006588972,0.002267504],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6130367,"threshold_uncertainty_score":0.9999426,"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."}}