{"id":"W4414464276","doi":"10.1109/acdsa65407.2025.11166298","title":"Optimizing Medical Response Time through Deep Learning Based Accident Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"IoT and GPS-based Vehicle Safety Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Accident (philosophy); Deep learning; Intervention (counseling); Law enforcement; Accident investigation; Emergency response; Response time","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":[0.0007410177,0.000151958,0.0001847168,0.0001291764,0.0001345348,0.00004384674,0.0001426896,0.0001902069,0.0006201446],"category_scores_gemma":[0.0002185224,0.0001474827,0.00008623496,0.0003318649,0.00001995396,0.0001306395,0.00002854652,0.0003345633,0.0002957751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001534379,"about_ca_system_score_gemma":0.00004958979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003623333,"about_ca_topic_score_gemma":0.00004941291,"domain_scores_codex":[0.9988337,0.0001640797,0.0002881962,0.00017933,0.0002577805,0.0002769325],"domain_scores_gemma":[0.9993278,0.0003631606,0.000021187,0.0001793525,0.00003678377,0.00007170394],"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":[0.0009036204,0.00004943064,0.0008393636,0.0001896937,0.0001709998,0.00007544924,0.0008217847,0.8693391,0.04919443,0.0001165377,0.001664367,0.07663522],"study_design_scores_gemma":[0.0006409415,0.00004234696,0.0007103428,0.0001143446,0.00001421564,0.000004763285,0.0001285049,0.9372223,0.04139831,0.00001479679,0.019532,0.0001771133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1137458,0.0004172872,0.8638584,0.0006831585,0.0009132905,0.0002303277,3.394636e-7,0.001567907,0.01858348],"genre_scores_gemma":[0.9960365,0.00001230184,0.001176139,0.0001969072,0.0001015729,0.00002247724,0.000004090827,0.00002800317,0.002421984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8822907,"threshold_uncertainty_score":0.6790149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004496256905174264,"score_gpt":0.224984610335058,"score_spread":0.2204883534298837,"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."}}