{"id":"W901342750","doi":"","title":"Micro-Data Collection and Development of Trip Generation Models of Commercial Vehicles: An Application for Windsor, Ontario","year":2014,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Urban and Freight Transport Logistics","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Windsor; Data collection; Computer science; Transport engineering; Environmental science; Engineering; Statistics; Mathematics","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":[0.001737216,0.0004856182,0.000396513,0.0008394716,0.0009463744,0.000852628,0.001069763,0.0004101183,0.005114253],"category_scores_gemma":[0.005393547,0.0004964491,0.0007553998,0.002207865,0.0002322254,0.0005385269,0.000511875,0.0003568175,0.000782684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008852174,"about_ca_system_score_gemma":0.009290176,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9133036,"about_ca_topic_score_gemma":0.9342132,"domain_scores_codex":[0.9994765,0.0001757945,0.00003981337,0.0001200601,0.0001288058,0.00005905155],"domain_scores_gemma":[0.9978125,0.0009073422,0.0002228628,0.0002412785,0.0007099662,0.0001059008],"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":[0.0003132973,0.0002352637,0.2992834,0.0005278863,0.0002713233,0.0006731382,0.003104676,0.5134593,0.002136038,0.01490028,0.02417009,0.1409255],"study_design_scores_gemma":[0.00005812227,0.00007486564,0.08522701,0.00007704931,0.00004325484,0.00009051912,0.001646553,0.8877342,0.0007092941,0.002207454,0.02206919,0.00006253666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7074096,0.0005863632,0.2074792,0.001700234,0.00007791009,0.001883713,0.06066351,0.002842091,0.0173574],"genre_scores_gemma":[0.8338315,0.0008895465,0.1302183,0.00006267458,0.00002067526,0.001243837,0.01948772,0.0002254467,0.01402029],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08669645,"threshold_uncertainty_score":0.174414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08057014986420842,"score_gpt":0.2143625468256319,"score_spread":0.1337923969614234,"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."}}