{"id":"W1542555304","doi":"","title":"Probabilistic Models for Discriminating Road Surface Conditions Based on Friction Measurements","year":2008,"lang":"en","type":"article","venue":"Transportation Research Board 87th Annual MeetingTransportation Research Board","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Road surface; Snow; Probabilistic logic; Statistical model; Logit; Snow cover; Aggregate (composite); Environmental science; Cover (algebra); Logistic regression; Field (mathematics); Statistics; Meteorology; Econometrics; Mathematics; Engineering; Geography; Civil engineering","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":[0.00398974,0.001093396,0.00113279,0.002358691,0.0003568138,0.001570038,0.002122173,0.00133193,0.002557022],"category_scores_gemma":[0.01538593,0.0007729123,0.001244776,0.001713847,0.001390811,0.002604716,0.001156321,0.001104102,0.0007286546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001085298,"about_ca_system_score_gemma":0.0005234513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008955882,"about_ca_topic_score_gemma":0.007814602,"domain_scores_codex":[0.9978673,0.0006888051,0.0001294579,0.0005513969,0.0004794012,0.000283557],"domain_scores_gemma":[0.9886108,0.008450158,0.00154399,0.0005994574,0.0006418889,0.0001536222],"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":[0.0001498853,0.00008191956,0.01509548,0.00005187071,0.00009684636,0.00008179147,0.0001092361,0.941267,0.0007301311,0.02244784,0.0006534216,0.01923468],"study_design_scores_gemma":[0.000009304144,0.00002506741,0.003074123,0.000006123469,0.00001768078,0.00003223924,0.00001685595,0.9838882,0.0001555374,0.01254417,0.0002103115,0.00002040201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1833345,0.0002702327,0.8117118,0.0003777698,0.00002948883,0.00009660931,0.001312872,0.0006236606,0.002243066],"genre_scores_gemma":[0.9662487,0.0003048369,0.02920929,0.00007375288,0.00005496536,0.0002072803,0.001172018,0.00004847356,0.002680762],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008955882,"threshold_uncertainty_score":0.02110004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1235939572732118,"score_gpt":0.3612138661551467,"score_spread":0.2376199088819349,"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."}}