{"id":"W4409537179","doi":"10.1007/978-3-031-85933-5_2","title":"Analysis of Driver Attention to Objects While Driving","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Thesaurus; Information retrieval; Artificial intelligence","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":[0.0000861931,0.0002256488,0.0002089128,0.0007013431,0.000129445,0.0002881827,0.0001785636,0.0001544734,0.001943319],"category_scores_gemma":[0.0003734158,0.0000908528,0.0002103896,0.000448934,0.00006385915,0.0001657401,0.0001524904,0.0001133608,0.0004299782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002152751,"about_ca_system_score_gemma":0.0001632323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007492873,"about_ca_topic_score_gemma":0.008321238,"domain_scores_codex":[0.999945,0.000006828712,0.000002204283,0.00001665364,0.00001747238,0.00001178445],"domain_scores_gemma":[0.9998512,0.00008429906,0.000009511179,0.000007206689,0.00003863363,0.000009052162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001671369,0.0002776449,0.06607429,0.0002779682,0.0002279034,0.0003283996,0.001030547,0.006473177,0.2647223,0.001113445,0.004946347,0.6528566],"study_design_scores_gemma":[0.00003433752,0.0008253699,0.7729054,0.00003925374,0.0002664183,0.0008810972,0.001125243,0.1439761,0.07067379,0.001170687,0.008044865,0.00005738958],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9583087,0.001303263,0.03231285,0.0000539831,0.00004328812,0.00003467355,0.0006117746,0.0002310798,0.007100326],"genre_scores_gemma":[0.9873561,0.0004427361,0.004878076,0.00001903941,0.00001533294,0.00001645505,0.0007330985,0.00003630153,0.006502897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007492873,"threshold_uncertainty_score":0.01489854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02592210729298989,"score_gpt":0.2870758553090325,"score_spread":0.2611537480160426,"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."}}