{"id":"W2158326259","doi":"10.1139/cjce-2015-0222","title":"Lateral coefficient of friction for characterizing winter road conditions","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Natural Resources; BP (Canada)","funders":"University of Alberta","keywords":"Snow; Environmental science; Dry ice; Road surface; Coefficient of friction; Geology; Atmospheric sciences; Meteorology; Materials science; Geomorphology; Composite material; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000191794,0.00006363262,0.0001149456,0.0001165093,0.00003114447,0.00002062312,0.00008205134,0.00003242579,0.0002291104],"category_scores_gemma":[0.00006688392,0.00006508093,0.00005166302,0.00007691987,0.00003187339,0.000209588,0.000007406767,0.00005146227,0.000008441731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000246794,"about_ca_system_score_gemma":0.00006535005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007271611,"about_ca_topic_score_gemma":0.009590475,"domain_scores_codex":[0.9994538,0.000007472837,0.0002372878,0.00005563211,0.00009263005,0.0001531107],"domain_scores_gemma":[0.9995199,0.000009317592,0.0001163713,0.0000592458,0.00003592974,0.0002591842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005721334,0.0000324857,0.1346038,0.00007108441,0.0001072735,0.00004350235,0.003232596,0.4534888,0.3968712,0.0002940972,0.01040938,0.0007884537],"study_design_scores_gemma":[0.003209147,0.0008836582,0.738938,0.0005585433,0.000171236,0.001534279,0.000523989,0.06878705,0.05806125,0.0004628139,0.1260175,0.0008525765],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991835,0.00001609518,0.006109225,0.00007957785,0.001644498,0.00006920141,0.00002234191,0.000005652397,0.0002183807],"genre_scores_gemma":[0.999426,5.757792e-7,0.0004032046,0.00001858928,0.000121801,0.000002360865,0.000003944587,0.00001033645,0.00001319479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6043342,"threshold_uncertainty_score":0.535171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01070224071801124,"score_gpt":0.194574265349399,"score_spread":0.1838720246313878,"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."}}