{"id":"W2023651697","doi":"10.1109/rose.2012.6402636","title":"Evolving sensor environments with visual attention: An experimental exploration","year":2012,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Viewpoints; Computer science; Identification (biology); Artificial intelligence; Set (abstract data type); Feature (linguistics); Object (grammar); Cognitive neuroscience of visual object recognition; Machine learning; Visualization; Computational model; Data science; Human–computer interaction; Computer vision","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":[0.0001445688,0.0001060978,0.000067947,0.00006411404,0.0001788272,0.0001249844,0.0001358242,0.00003389917,0.000150554],"category_scores_gemma":[0.000002129282,0.0000856577,0.0000290186,0.000142082,0.00002255191,0.004506253,0.00005593895,0.00005068201,0.0002778509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006247364,"about_ca_system_score_gemma":0.000005608514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007325652,"about_ca_topic_score_gemma":0.000001921388,"domain_scores_codex":[0.9990329,0.00006997871,0.0001339896,0.0002311562,0.0003054299,0.0002265918],"domain_scores_gemma":[0.9995908,0.000005308913,0.00005116562,0.0002008498,0.00001398714,0.0001379296],"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":[0.00005709348,0.003706001,0.05113458,0.0000120394,0.00005009578,0.000007848769,0.006486662,0.0001804674,0.8865966,0.02471166,0.0003416629,0.02671533],"study_design_scores_gemma":[0.002861007,0.00377784,0.2533303,0.00003970791,0.0000300302,0.000206094,0.01010318,0.2656195,0.4562457,0.0001763178,0.006036979,0.001573286],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4499821,0.00001714062,0.5483409,0.00005789082,0.0002363322,0.00008347235,1.005158e-7,0.0001201451,0.001161867],"genre_scores_gemma":[0.9833874,0.000001362977,0.0153245,0.000132724,0.0001130152,0.0000231121,0.000005424351,0.000008042359,0.001004383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5334053,"threshold_uncertainty_score":0.3571303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03151561053383137,"score_gpt":0.2946089004959739,"score_spread":0.2630932899621425,"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."}}