{"id":"W4387717450","doi":"10.1109/tce.2023.3325335","title":"A Dual Channel Cyber–Physical Transportation Network for Detecting Traffic Incidents and Driver Emotion","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Graph; Dual (grammatical number); Traffic congestion; Intelligent transportation system; Attention network; Channel (broadcasting); Artificial intelligence; Computer network; Theoretical computer science; Transport engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002470482,0.0001714829,0.0002017922,0.0002031463,0.0004461326,0.0001051852,0.0001450892,0.00007404304,0.000003770238],"category_scores_gemma":[0.000003379522,0.0001818311,0.0001588547,0.000806952,0.00002916892,0.0003023476,0.000001146002,0.0002038613,0.00002298847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005503204,"about_ca_system_score_gemma":0.00005734116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007973383,"about_ca_topic_score_gemma":0.0001580883,"domain_scores_codex":[0.9985949,0.00004635709,0.0002369564,0.0004282272,0.0002378448,0.0004557335],"domain_scores_gemma":[0.9993553,0.000217631,0.00008858329,0.0001934489,0.00006635662,0.00007867454],"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":[0.0001210243,0.000338554,0.0001424199,0.00005983558,0.0006645565,0.000009378951,0.007952654,0.500183,0.003806471,0.001692129,0.0006664018,0.4843636],"study_design_scores_gemma":[0.0008812267,0.0002485219,0.0009910822,0.00003369716,0.0001352613,0.000005489827,0.0001083898,0.9914132,0.004849039,0.0005744013,0.0004762737,0.0002833951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4981565,0.00006209232,0.5010554,0.0001195644,0.0002652107,0.0001690415,0.000005359719,0.0001643538,0.000002531349],"genre_scores_gemma":[0.9980913,0.0001725684,0.001396604,0.00005144827,0.00007754557,0.00006877093,0.00001756938,0.00001924281,0.0001049117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4999349,"threshold_uncertainty_score":0.7414855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01835294832823025,"score_gpt":0.2627110885569494,"score_spread":0.2443581402287191,"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."}}