{"id":"W614873424","doi":"","title":"Imputation Algorithm of Blood Alcohol Content Levels for Drivers and Pedestrians in Fatal Collisions","year":2011,"lang":"en","type":"article","venue":"Transportation Research Board 90th Annual MeetingTransportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Imputation (statistics); Blood alcohol; Computer science; Collision; Blood alcohol content; Alcohol; Algorithm; Poison control; Medicine; Injury prevention; Medical emergency; Computer security; Missing data; Machine learning; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01367692,0.0005837019,0.001192059,0.001599814,0.001372596,0.001285108,0.002816862,0.001385833,0.003415289],"category_scores_gemma":[0.03445333,0.00053742,0.001584382,0.002629047,0.0004622712,0.0008971043,0.001582545,0.002021173,0.001167364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008741861,"about_ca_system_score_gemma":0.003062076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01709106,"about_ca_topic_score_gemma":0.01357028,"domain_scores_codex":[0.9966682,0.001629457,0.0003518196,0.0006497214,0.0004418031,0.0002590515],"domain_scores_gemma":[0.9874933,0.006566747,0.0007251686,0.001832174,0.003209586,0.0001729805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001229379,0.0003887391,0.1524467,0.0001498786,0.0007102339,0.0005670163,0.001234024,0.2155204,0.002084885,0.01741925,0.016981,0.5912685],"study_design_scores_gemma":[0.0001684083,0.0001187584,0.01700382,0.00003749929,0.0001567041,0.0003556829,0.0002407851,0.9576492,0.002449172,0.01741638,0.004347659,0.00005591503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05674811,0.0001243016,0.9391359,0.0005437579,0.00005350391,0.0002722013,0.001015307,0.001094022,0.001012805],"genre_scores_gemma":[0.254068,0.0001055935,0.7382562,0.0001453871,0.00004437068,0.0005523701,0.003901776,0.0001411553,0.002785226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01709106,"threshold_uncertainty_score":0.07233137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1291747270134107,"score_gpt":0.3521279340160241,"score_spread":0.2229532070026134,"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."}}