{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008714516,0.0007171711,0.001162989,0.0005469256,0.0006491517,0.001245045,0.001589023,0.001511549,0.01447374],"category_scores_gemma":[0.01055672,0.000653661,0.0003191823,0.0007463817,0.000672649,0.006065442,0.002302202,0.001488797,0.002628601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005336183,"about_ca_system_score_gemma":0.000546671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009027954,"about_ca_topic_score_gemma":0.001065083,"domain_scores_codex":[0.9994625,0.0001610983,0.00003858783,0.0001088024,0.0001437451,0.0000852609],"domain_scores_gemma":[0.994637,0.003373137,0.0002450513,0.0009762657,0.0004207055,0.0003478285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002194841,0.0003325964,0.001205565,0.0006014571,0.00005416574,0.0002502079,0.0005105542,0.02460987,0.1389635,0.3129217,0.02630356,0.4920521],"study_design_scores_gemma":[0.0002090726,0.0002660985,0.000769707,0.00004423325,0.00003401318,0.000205205,0.0001259974,0.43016,0.04441192,0.5073715,0.01635078,0.00005142994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06039419,0.001076637,0.9283291,0.0006984998,0.0001970867,0.0001202839,0.0003923374,0.003961725,0.004830134],"genre_scores_gemma":[0.6169947,0.0005986976,0.3697297,0.0002806091,0.0001949786,0.000402807,0.001182098,0.001573214,0.009043155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01447374,"threshold_uncertainty_score":0.04841948,"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."}}