{"id":"W4310525280","doi":"10.3390/stats5040075","title":"A Bayesian One-Sample Test for Proportion","year":2022,"lang":"en","type":"article","venue":"Stats","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; McMaster University; University of Toronto","funders":"","keywords":"Mathematics; Statistics; A priori and a posteriori; Divergence (linguistics); Bayesian probability; Null hypothesis; Bernoulli's principle; Sample size determination; Kullback–Leibler divergence; Null (SQL); Binomial distribution; Measure (data warehouse); Binomial (polynomial); Negative binomial distribution; Sample (material); Computer science; Poisson distribution; Physics; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004346139,0.00007974567,0.0001507313,0.00003768228,0.0002371419,0.0000207679,0.0001045512,0.00001853001,0.001114898],"category_scores_gemma":[0.003811523,0.00007703579,0.00004472405,0.0001179745,0.00002870902,0.00003194368,0.00005671695,0.0001022191,0.000003935199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005118247,"about_ca_system_score_gemma":0.00005962257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002237607,"about_ca_topic_score_gemma":0.00001065697,"domain_scores_codex":[0.9991022,0.00006133469,0.0002180865,0.0001850599,0.0002179404,0.0002153977],"domain_scores_gemma":[0.9969925,0.002624595,0.00009186341,0.0001766423,0.00005474628,0.0000596535],"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":[0.00003715452,0.0003080128,0.0006163394,0.0001310268,0.00001371639,0.000003686747,0.0003321467,7.571685e-7,0.0006131507,0.9028009,0.004737348,0.09040581],"study_design_scores_gemma":[0.0002384523,0.0003357051,0.0003174413,0.000007756486,0.0000221899,0.00000290966,0.0001360278,0.001582925,0.0003209753,0.990144,0.006777429,0.0001141609],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006891981,0.000006614211,0.9962543,0.0003174062,0.0001181143,0.0004814255,0.0007992797,0.00006805779,0.001265623],"genre_scores_gemma":[0.08526986,8.946512e-7,0.9138944,0.0001067722,0.00005759342,0.0003752679,0.00002927793,0.00002033424,0.0002456142],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09029166,"threshold_uncertainty_score":0.9997982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1142653444930024,"score_gpt":0.3971139948043215,"score_spread":0.2828486503113191,"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."}}