{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002879017,0.000139066,0.0005053623,0.0001319084,0.0001720503,0.0003882592,0.002350684,0.00007768924,0.0003342957],"category_scores_gemma":[0.008925041,0.0001004206,0.00009780459,0.0003541814,0.00006838528,0.0008716666,0.001093047,0.0006913344,0.00002749648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007820677,"about_ca_system_score_gemma":0.0004019968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000042996,"about_ca_topic_score_gemma":0.000001990441,"domain_scores_codex":[0.9973437,0.0001974662,0.0006307444,0.0003199519,0.001020345,0.0004877759],"domain_scores_gemma":[0.993601,0.004254941,0.0001863098,0.0006975823,0.001021929,0.0002381988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.008569049,0.006201879,0.009470825,0.003872115,0.001571609,0.0005259279,0.001859267,0.00428198,0.005137671,0.1623037,0.107653,0.688553],"study_design_scores_gemma":[0.001156301,0.0004981607,0.00005569748,0.0003603747,0.000029098,0.00003037594,0.0001034359,0.5309504,0.00003412822,0.4655977,0.001014982,0.0001693556],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04495273,0.00004002798,0.9534198,0.0004215537,0.0001578156,0.0007354377,0.00014509,0.00001054039,0.0001169817],"genre_scores_gemma":[0.1741557,0.00001804299,0.8250952,0.00006002732,0.0003356929,0.000009882377,0.000009816304,0.00003824691,0.0002774046],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6883836,"threshold_uncertainty_score":0.9994232,"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."}}