{"id":"W2909640191","doi":"10.5334/jors.202","title":"BayesFit: A tool for modeling psychophysical data using Bayesian inference","year":2019,"lang":"en","type":"article","venue":"Journal of Open Research Software","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Python (programming language); Computer science; Markov chain Monte Carlo; Inference; Programming language; Data mining; Bayesian probability; Source code; Software; Bayesian inference; Algorithm; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003747731,0.001635926,0.0014062,0.002014057,0.0008480052,0.003130553,0.00443673,0.001387636,0.07479697],"category_scores_gemma":[0.02203842,0.001476244,0.001777195,0.001375133,0.000963743,0.003146034,0.003408018,0.003006193,0.02240956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100472,"about_ca_system_score_gemma":0.002911081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004710297,"about_ca_topic_score_gemma":0.005638563,"domain_scores_codex":[0.9984201,0.000388975,0.0001232809,0.0002321475,0.0007508115,0.00008471452],"domain_scores_gemma":[0.9956064,0.002660284,0.000340038,0.0005068362,0.0007017531,0.0001846787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004096246,0.0003265262,0.006938752,0.00252483,0.0005737686,0.0004669223,0.0008048068,0.09397756,0.01102928,0.1115019,0.4390643,0.3323817],"study_design_scores_gemma":[0.0001152908,0.00005435472,0.001965392,0.0004159419,0.00006657775,0.0004153925,0.0000696479,0.6393727,0.00684811,0.2174087,0.133085,0.000182924],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.0010395,0.0001327526,0.9421516,0.0002339164,0.00009547848,0.0001578532,0.004183923,0.04800222,0.004002742],"genre_scores_gemma":[0.03108768,0.0004489954,0.922717,0.0007312504,0.0001098238,0.001446423,0.00580229,0.03212853,0.00552802],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.07479697,"threshold_uncertainty_score":0.2502208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4857635740951576,"score_gpt":0.5568300648611997,"score_spread":0.07106649076604205,"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."}}