{"id":"W3217737745","doi":"10.32920/ryerson.14655465.v1","title":"Evaluation of driver visual demand at different traffic volumes on complex two-dimensional multi-lane highway alignments","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Traffic and Road Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Western University","funders":"","keywords":"Consistency (knowledge bases); Computer science; Traffic volume; Geometric design; Process (computing); Transport engineering; Software; Volume (thermodynamics); Visual Basic; Statistical analysis; Simulation; Mathematics; Engineering; Artificial intelligence; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004991762,0.0003782668,0.0002654281,0.0005934485,0.0001596296,0.000497505,0.0002618783,0.0003683323,0.001703268],"category_scores_gemma":[0.002487949,0.0001396039,0.0002797993,0.0003830465,0.0001317183,0.0004918193,0.000352484,0.0001553474,0.0002686449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002637956,"about_ca_system_score_gemma":0.0001899592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002774167,"about_ca_topic_score_gemma":0.003916259,"domain_scores_codex":[0.9995079,0.0001206946,0.00003165942,0.00007679514,0.0002089526,0.00005394964],"domain_scores_gemma":[0.9984267,0.0007805338,0.000203877,0.00008736913,0.0004126347,0.00008893427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005904907,0.002392036,0.6510457,0.0005963256,0.0002851759,0.000317959,0.001852099,0.07103543,0.1855808,0.0006772912,0.0007858491,0.07952642],"study_design_scores_gemma":[0.00006656229,0.004881884,0.8447349,0.00002339633,0.0001159196,0.000207481,0.002265222,0.1046272,0.04187998,0.000305878,0.0008126482,0.00007884132],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985877,0.000008951071,0.0009313719,0.000003972999,0.000001633099,0.00001016261,0.0001009174,0.00001102956,0.0003441921],"genre_scores_gemma":[0.9987117,0.00001605357,0.0007891651,0.000003452763,0.000001094077,0.00001535432,0.0002385918,0.000004953809,0.0002196152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002774167,"threshold_uncertainty_score":0.005697966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0453147308991599,"score_gpt":0.2928489517416932,"score_spread":0.2475342208425333,"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."}}