{"id":"W4386175201","doi":"10.46298/cst.12006","title":"Measuring varied signs of urban sprawl with data from travel surveys and censuses: multi-perspective study of the Greater Montreal Area","year":2003,"lang":"en","type":"article","venue":"Les Cahiers scientifiques du transport","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université Laval","funders":"","keywords":"Urban sprawl; Geography; Census; Regional science; Human settlement; Population; Perspective (graphical); Economic geography; Urban planning; Cartography; Sociology; Demography; Computer science; Archaeology","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":[0.003721862,0.0002615577,0.0004826674,0.0001072362,0.0006809881,0.00006406941,0.0009573847,0.0001431161,0.00008703687],"category_scores_gemma":[0.00009740439,0.0001802844,0.00009306848,0.000689341,0.002407248,0.0005051079,0.00002340486,0.0002452459,3.401116e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009453302,"about_ca_system_score_gemma":0.000303326,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09405118,"about_ca_topic_score_gemma":0.3777812,"domain_scores_codex":[0.9964007,0.0009319952,0.0005159965,0.0009144121,0.0008536301,0.0003833286],"domain_scores_gemma":[0.9980366,0.0001378236,0.0002719979,0.0009377483,0.0004488513,0.0001670142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000613541,0.0007313283,0.9008663,0.00002227471,0.0001309507,0.00001090576,0.0972451,0.000009268833,0.0006270088,0.0002244817,0.00001237679,0.00005869293],"study_design_scores_gemma":[0.001217242,0.0000751377,0.9497039,0.00006513002,0.0002562613,2.696623e-7,0.0457053,0.00001973971,0.002485567,0.0002036055,0.00003965304,0.0002282327],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901193,0.0001345458,0.00519524,0.00003961245,0.0001587274,0.0009379553,0.0006089089,0.00004315298,0.002762516],"genre_scores_gemma":[0.9989641,0.00001498159,0.00052857,0.00000836622,0.00002407775,0.00001171634,0.00004018503,0.00002116141,0.000386896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2837301,"threshold_uncertainty_score":0.9119816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08647860660031989,"score_gpt":0.2753588613217792,"score_spread":0.1888802547214593,"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."}}