{"id":"W209594168","doi":"10.3758/s13428-015-0600-5","title":"PsiMLE: A maximum-likelihood estimation approach to estimating psychophysical scaling and variability more reliably, efficiently, and flexibly","year":2015,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Bayesian probability; Statistics; Gaussian; Algorithm; Variance (accounting); Artificial intelligence; Mathematics; Pattern recognition (psychology)","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":[],"consensus_categories":[],"category_scores_codex":[0.01149548,0.0002252233,0.0002986507,0.0003182814,0.0005442441,0.0004707738,0.000311867,0.0001508315,0.00001340149],"category_scores_gemma":[0.007175003,0.0002030251,0.0000390748,0.001180411,0.0004761984,0.0002940411,0.0004063473,0.0006545332,0.00002461538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001072377,"about_ca_system_score_gemma":0.0001439407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004478707,"about_ca_topic_score_gemma":2.827159e-7,"domain_scores_codex":[0.9943506,0.002496009,0.0003597891,0.001064379,0.001067545,0.0006616603],"domain_scores_gemma":[0.9977375,0.0006838515,0.00006630632,0.0004503229,0.0002674244,0.0007945668],"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":[0.0001420375,0.0006276759,0.0001371276,0.0001242808,0.000001464697,0.00000563407,0.00405947,0.0003725541,0.3315348,0.001110514,0.0001375942,0.6617468],"study_design_scores_gemma":[0.001445749,0.0008854965,0.002858668,0.0001502364,0.00004048493,0.0001370932,0.001540941,0.8798836,0.0653635,0.04677136,0.000297307,0.000625519],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.470291,0.00001230777,0.5277348,0.0002693428,0.0001544086,0.0006622754,0.00000725932,0.0001196267,0.0007488976],"genre_scores_gemma":[0.4036427,0.000001948055,0.5958931,0.0001283448,0.00006183236,0.0001843828,0.000003268342,0.00002767444,0.00005679771],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8795111,"threshold_uncertainty_score":0.8589667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3820229668864004,"score_gpt":0.5778684082558168,"score_spread":0.1958454413694163,"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."}}