{"id":"W4403210088","doi":"10.1109/tits.2024.3443832","title":"Driver Drowsiness Detection Based on Joint Human Face and Facial Landmark Localization With Cheap Operations","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Science and Technology Development Fund","keywords":"Landmark; Computer vision; Artificial intelligence; Face (sociological concept); Joint (building); Face detection; Computer science; Facial recognition system; Pattern recognition (psychology); Speech recognition; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001865482,0.0003125191,0.0002527052,0.0005032236,0.0004236633,0.0001699388,0.00007334218,0.0002731309,0.0004179268],"category_scores_gemma":[0.0000011049,0.0002718834,0.0001072174,0.0006216498,0.0000875635,0.0001892605,1.221652e-7,0.0003959586,0.0001607961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122468,"about_ca_system_score_gemma":0.00004407623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000790123,"about_ca_topic_score_gemma":0.001755327,"domain_scores_codex":[0.9981152,0.000137638,0.0005620664,0.0005827646,0.0003534642,0.0002488596],"domain_scores_gemma":[0.9993538,0.00007910725,0.00005727357,0.0002614709,0.0001247715,0.0001235707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003166175,0.0003979832,0.0002942753,0.0001017392,0.0003358852,0.00004424106,0.006550748,0.9641024,0.001133738,0.002237735,0.00008480387,0.02439981],"study_design_scores_gemma":[0.01100197,0.007682761,0.01584614,0.01035809,0.003290882,0.000119903,0.02091017,0.7553397,0.1571499,0.00008546772,0.01300092,0.005214077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1063092,0.0001124004,0.8892912,0.0001057904,0.002275124,0.0008446786,0.0001277957,0.0003614618,0.0005723287],"genre_scores_gemma":[0.9986058,0.00002220175,0.00004095297,0.00007690131,0.00009314901,0.0004084154,0.0001397283,0.000065698,0.000547184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8922966,"threshold_uncertainty_score":0.9999734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02632102390891469,"score_gpt":0.2791237909628529,"score_spread":0.2528027670539383,"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."}}