{"id":"W3174466774","doi":"10.1139/cjce-2020-0656","title":"Exploring vehicle probe data as a resource to enhance network-wide traffic volume estimates","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Transport Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Transport Canada","keywords":"Traffic volume; Truck; Volume (thermodynamics); Traffic count; Traffic flow (computer networking); Environmental science; Transport engineering; Statistics; Computer science; Engineering; Mathematics; Automotive engineering","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":[0.0002980111,0.0001774487,0.0002367691,0.00027084,0.00006366222,0.0001227344,0.0004910145,0.00004896215,0.00006964776],"category_scores_gemma":[0.000255206,0.0002149632,0.00005593117,0.0004539528,0.00001165279,0.0005206667,0.00005364129,0.0003189426,0.00001938887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001666677,"about_ca_system_score_gemma":0.0001717168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005935711,"about_ca_topic_score_gemma":0.01317521,"domain_scores_codex":[0.9988028,0.00001084159,0.0003646163,0.0001799232,0.0001666033,0.0004752493],"domain_scores_gemma":[0.9987952,0.00004559827,0.00003927714,0.0004154576,0.00006882544,0.0006356169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001811692,0.000003523803,0.00007864911,0.00006589122,0.00006332007,0.0003154873,0.0002246601,0.9032869,0.00045745,0.0000736923,0.08654388,0.008884729],"study_design_scores_gemma":[0.0001401951,0.0000518635,0.001261246,0.0006623223,0.00004808135,0.0001516363,0.0001138485,0.3704492,0.001676175,0.00001336335,0.6250761,0.0003559669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1297964,0.006874361,0.8481755,0.001846161,0.003123796,0.000423923,0.00004393835,0.002864164,0.006851757],"genre_scores_gemma":[0.9927468,0.0001202031,0.006471344,0.0001435808,0.0003455699,0.00001232965,0.00001123611,0.00006318014,0.00008580768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8629504,"threshold_uncertainty_score":0.8765946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02524205106043783,"score_gpt":0.2142214237232692,"score_spread":0.1889793726628314,"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."}}