{"id":"W2241036302","doi":"10.2495/ut030221","title":"TRAVEL DEMAND FORECASTING AND TDM MEASURES: THE EXAMPLE OF MONTREAL'S SOUTH SHORE, 2001-2021","year":2003,"lang":"en","type":"article","venue":"WIT transactions on the built environment","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Demand forecasting; Demand management; Shore; Transport engineering; Traffic congestion; Work (physics); Business; Regional science; Operations research; Geography; Economics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007691314,0.0000981276,0.0001008327,0.00003335939,0.0006849908,0.00002610239,0.0001034353,0.00005543488,0.000375891],"category_scores_gemma":[0.0000196076,0.00006413567,0.00005260126,0.0001089745,0.0002528139,0.0000580521,6.943325e-7,0.0001160541,0.000005260953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003035723,"about_ca_system_score_gemma":0.00003056726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001903394,"about_ca_topic_score_gemma":0.0009233024,"domain_scores_codex":[0.9989347,0.0002138788,0.0001816127,0.00016874,0.0003193265,0.0001816719],"domain_scores_gemma":[0.999444,0.0002285005,0.00007527837,0.0001806522,0.00001231476,0.00005924545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001394292,0.0005515266,0.007650474,0.00002831169,0.00031624,0.00000504593,0.2690834,0.6578185,0.0006590592,0.00986032,0.0003736769,0.05351407],"study_design_scores_gemma":[0.007344019,0.001181718,0.351646,0.0005844395,0.002436134,0.00003691703,0.4260119,0.02866093,0.01965664,0.01442447,0.1450996,0.002917151],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1465402,0.0003722388,0.8426319,0.002211102,0.00011943,0.0006908189,0.00005918343,0.00002795352,0.007347203],"genre_scores_gemma":[0.997821,0.0004019917,0.0008372983,0.00005709634,0.00001825647,0.0000349313,0.000003321734,0.000009838979,0.0008163011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8512808,"threshold_uncertainty_score":0.5268465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04973730286399573,"score_gpt":0.229862362510944,"score_spread":0.1801250596469483,"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."}}