{"id":"W4240579883","doi":"10.1177/0361198106195000109","title":"Using Macrolevel Collision Prediction Models in Road Safety Planning Applications","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Okanagan University College; University of British Columbia, Okanagan Campus","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SAFER; Transport engineering; Collision; Grid; Computer science; Engineering; Computer security; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002197401,0.0006325747,0.0005978149,0.0006877106,0.0004040909,0.001232143,0.001133887,0.000509586,0.0009233372],"category_scores_gemma":[0.01240665,0.0004320615,0.0004144117,0.0009032484,0.000310514,0.00110118,0.0009164808,0.0008056544,0.0002163138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001153885,"about_ca_system_score_gemma":0.001564011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03536427,"about_ca_topic_score_gemma":0.04239316,"domain_scores_codex":[0.9988567,0.0005574368,0.00006734756,0.0001831327,0.0002723921,0.00006288843],"domain_scores_gemma":[0.9938129,0.004184924,0.0004204792,0.0004350672,0.001023614,0.0001229987],"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":[0.00008067094,0.00009116195,0.01395618,0.00005108248,0.00005943398,0.00004567159,0.00007840588,0.9396154,0.0005439504,0.001335112,0.0004415994,0.0437014],"study_design_scores_gemma":[0.0000134615,0.00004746229,0.001411966,0.000008671745,0.00001500331,0.00001213361,0.00003270006,0.996469,0.0004854593,0.0009850711,0.0005051544,0.00001395626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3325375,0.0002542598,0.6596405,0.000332772,0.00005427492,0.0003287406,0.0006625275,0.002039614,0.004149843],"genre_scores_gemma":[0.8123934,0.0001753235,0.186031,0.00007597974,0.00001759828,0.0002760096,0.0003877611,0.0000804575,0.0005625162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03536427,"threshold_uncertainty_score":0.07031685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09188256997714077,"score_gpt":0.3582997525723157,"score_spread":0.2664171825951749,"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."}}