{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004944047,0.0001535166,0.0003154223,0.000174581,0.0002026562,0.000008594654,0.0003486257,0.0002016829,0.0000166277],"category_scores_gemma":[0.00001048988,0.0002012806,0.00004790446,0.0001561334,0.0001146146,0.0005217497,0.0000553659,0.0001509582,0.000001343257],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001338564,"about_ca_system_score_gemma":0.0001084366,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005217612,"about_ca_topic_score_gemma":0.01986954,"domain_scores_codex":[0.999025,0.00003935374,0.0002972723,0.0002786466,0.0001918134,0.0001678879],"domain_scores_gemma":[0.9991461,0.00004257472,0.0001507736,0.000411659,0.0001574369,0.00009146531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001486616,0.0008084222,0.2599348,0.001383943,0.0006241169,0.000001731446,0.03132136,0.01817024,0.6374708,0.002710436,0.0008297061,0.04525786],"study_design_scores_gemma":[0.006751221,0.0005106116,0.6293914,0.0001224361,0.0004504806,0.000005675121,0.0008215643,0.2421898,0.107856,0.0009888818,0.009987767,0.0009240997],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8024085,0.00005110913,0.1968651,0.0000146873,0.00006526693,0.0002980355,0.00008545493,0.00003301087,0.0001788351],"genre_scores_gemma":[0.9607964,0.00001435407,0.03840874,0.000007793465,0.00003442648,0.000001201983,0.0006140334,0.00001967322,0.0001033845],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5296148,"threshold_uncertainty_score":0.9980153,"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."}}