{"id":"W2167092259","doi":"10.1167/5.2.5","title":"Classification images predict absolute efficiency","year":2005,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Eye Institute; National Institutes of Natural Sciences; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Observer (physics); Artificial intelligence; Pattern recognition (psychology); Contrast (vision); Range (aeronautics); Computer science; Perception; Contextual image classification; Task (project management); Image (mathematics); Mathematics; Computer vision; Psychology; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000467668,0.00006924794,0.00009754578,0.0001378758,0.000124505,0.00008070302,0.0001973185,0.00004606473,0.0003252],"category_scores_gemma":[0.0003246606,0.00005126411,0.00006924917,0.0001705479,0.00003904007,0.0004809964,0.00001942301,0.0001744228,0.0001804268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004506387,"about_ca_system_score_gemma":0.00004679486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.931376e-7,"about_ca_topic_score_gemma":9.007578e-8,"domain_scores_codex":[0.9989144,0.0000747107,0.0003372823,0.0001184951,0.0004365927,0.0001185678],"domain_scores_gemma":[0.9993541,0.00005485857,0.0003097624,0.00009316583,0.0001034187,0.00008469068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003145471,0.0001180975,0.000008207228,0.00001276816,4.924447e-7,0.000003316668,0.0001195904,0.00005276021,0.946607,0.0001900881,0.00309088,0.04976534],"study_design_scores_gemma":[0.002172028,0.002541083,0.0486489,0.0009150923,0.00003656417,0.0007484331,0.0002855188,0.06433398,0.8021033,0.001722798,0.07607735,0.0004149202],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9619618,0.000158733,0.02620944,0.003726995,0.0007738544,0.00007936606,0.000003225828,0.00004753486,0.00703907],"genre_scores_gemma":[0.9961393,0.0002507997,0.001611635,0.0007425447,0.0003458819,3.662414e-7,2.673879e-7,0.000008015797,0.0009011465],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1445037,"threshold_uncertainty_score":0.3560711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05258451301987026,"score_gpt":0.3619634818590628,"score_spread":0.3093789688391925,"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."}}