{"id":"W2149008138","doi":"10.1016/j.visres.2011.04.007","title":"Efficient bubbles for visual categorization tasks","year":2011,"lang":"en","type":"article","venue":"Vision Research","topic":"Face Recognition and Perception","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Engineering and Physical Sciences Research Council","keywords":"Categorization; Computer science; Visual search; Artificial intelligence; Observer (physics); Pattern recognition (psychology); Stimulus (psychology); Computer vision; Machine learning; Psychology; Cognitive 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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008578436,0.00006849594,0.00006964391,0.0002914441,0.0003575182,0.00006297638,0.0001487869,0.00007325653,0.001478911],"category_scores_gemma":[0.0007386474,0.00005771708,0.00004208509,0.0004782907,0.0001211552,0.00006054734,0.00006287141,0.000151566,0.001221649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003952286,"about_ca_system_score_gemma":0.00004730171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004142752,"about_ca_topic_score_gemma":0.000006097108,"domain_scores_codex":[0.9984306,0.0002494647,0.0001428006,0.0003321453,0.0005186977,0.0003263684],"domain_scores_gemma":[0.999233,0.0002857243,0.00002494352,0.0001361826,0.0002148249,0.0001053398],"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":[0.0002035969,0.0004172741,0.00008119728,0.00003753345,0.000001073868,0.000003102816,0.001015918,0.00005138796,0.9283581,0.004883069,0.003622844,0.06132491],"study_design_scores_gemma":[0.001054636,0.001303788,0.005034873,0.00004807757,0.000004053134,0.00000783124,0.0006832052,0.1221197,0.8464805,0.003623512,0.01938548,0.0002543033],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9729735,0.00001034596,0.01178518,0.0002886442,0.0002757746,0.0007833735,0.00002183952,0.00009739486,0.013764],"genre_scores_gemma":[0.9979789,0.00003433459,0.0003382772,0.00009788903,0.0000628611,0.0000771806,0.00001246887,0.00001513116,0.001382924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1220684,"threshold_uncertainty_score":0.999556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3435780748187133,"score_gpt":0.4800760344626666,"score_spread":0.1364979596439533,"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."}}