{"id":"W605666644","doi":"","title":"Road User Collision Prediction Using Motion Patterns Applied to Surrogate Safety Analysis","year":2014,"lang":"en","type":"article","venue":"Transportation Research Board 93rd Annual MeetingTransportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Collision; Computer science; Trajectory; Probabilistic logic; Motion (physics); Process (computing); Discretization; Simulation; Data mining; Artificial intelligence; Mathematics; Computer security","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":[0.0009289051,0.0003974684,0.0004799419,0.001264899,0.0001880228,0.0006486903,0.0006724781,0.0004426233,0.0006574046],"category_scores_gemma":[0.006655633,0.0002794098,0.0003731402,0.0008220111,0.0003991574,0.0008162985,0.0007769568,0.0004926947,0.0001638894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005721225,"about_ca_system_score_gemma":0.0007331664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006677396,"about_ca_topic_score_gemma":0.004482072,"domain_scores_codex":[0.9994523,0.0001630373,0.00003602172,0.0001165304,0.0001884216,0.0000437995],"domain_scores_gemma":[0.9977947,0.001014744,0.0003695058,0.0002529965,0.0004920646,0.00007599469],"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":[0.0001023911,0.00006981804,0.01808348,0.00003940392,0.00002526588,0.00007042538,0.00008428701,0.8941796,0.002581113,0.004177102,0.0002615525,0.08032547],"study_design_scores_gemma":[0.000001330022,0.00002050373,0.00119747,0.000002671607,0.000001245116,0.00001022868,0.000008091178,0.997633,0.0003820468,0.0006677136,0.00007279414,0.000002901783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1425477,0.00003887913,0.8560644,0.00006843258,0.00001297745,0.0000559537,0.0001135647,0.0003415575,0.0007563835],"genre_scores_gemma":[0.8950559,0.00004744022,0.1041874,0.00001128869,0.000007316042,0.00005040324,0.0002055982,0.00001940037,0.0004151488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006677396,"threshold_uncertainty_score":0.01327705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03333739205294465,"score_gpt":0.3224629244923699,"score_spread":0.2891255324394252,"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."}}