{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001790548,0.0006121271,0.0004375214,0.002868136,0.0003293074,0.001227587,0.0007810649,0.0003542945,0.0008597443],"category_scores_gemma":[0.01017819,0.0002851985,0.0002657426,0.003782718,0.0002100172,0.001467991,0.001050393,0.0004461805,0.0002811181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008516484,"about_ca_system_score_gemma":0.001596155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.104796,"about_ca_topic_score_gemma":0.1544152,"domain_scores_codex":[0.9992466,0.000281248,0.00003799043,0.0001245151,0.0002484406,0.00006120533],"domain_scores_gemma":[0.9958527,0.001995422,0.0004877928,0.0005241444,0.001050742,0.00008916494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001768098,0.000200936,0.491321,0.0002396096,0.0002306381,0.0002546397,0.001210435,0.2198804,0.01196694,0.004189677,0.0034258,0.2669032],"study_design_scores_gemma":[0.00002514058,0.0002623374,0.2865112,0.0001549134,0.0001294509,0.0002208455,0.002363582,0.6726063,0.01101068,0.00652302,0.0200772,0.0001153306],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6567915,0.0004364366,0.3194181,0.0008107642,0.00005564046,0.0003236246,0.01097107,0.002449305,0.008743537],"genre_scores_gemma":[0.8696115,0.0003073772,0.1240026,0.00006459494,0.00002368171,0.0001429985,0.005050879,0.00008540937,0.0007111026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.104796,"threshold_uncertainty_score":0.208372,"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."}}