{"id":"W2017326332","doi":"10.1007/s11111-015-0234-7","title":"Analyzing the impact of urban planning on population distribution in the Montreal metropolitan area using a small-area microsimulation projection model","year":2015,"lang":"en","type":"article","venue":"Population and Environment","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Metropolitan area; Urban sprawl; Microsimulation; Geography; Projections of population growth; Population; Regional science; Urban planning; Population projection; Distribution (mathematics); Regional planning; Urban area; Transport engineering; Population growth; Economics; Demography; Economy; Civil engineering; Engineering; Sociology","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":[0.002062174,0.0001410704,0.0001763221,0.0002156232,0.0002066881,0.0001099355,0.0001242236,0.00007061668,0.000003998336],"category_scores_gemma":[0.0002123256,0.00007897305,0.00009415097,0.0003895642,0.00003670423,0.0002222238,0.00002520807,0.0001102759,0.000001033242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000389192,"about_ca_system_score_gemma":0.00001238918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005182063,"about_ca_topic_score_gemma":0.0001223576,"domain_scores_codex":[0.9981341,0.0002744931,0.0005653106,0.0002839069,0.000578739,0.000163486],"domain_scores_gemma":[0.9990928,0.0001719238,0.0003675019,0.0002782044,0.00003935899,0.00005024183],"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":[0.00005578459,0.00003679448,0.3668252,8.76968e-7,0.000004997865,1.425868e-7,0.001138396,0.6295388,0.00007156784,0.0001759431,0.00001889402,0.002132581],"study_design_scores_gemma":[0.0001857111,0.00004877822,0.4366621,0.00001088105,0.00001439925,0.000001292892,0.001117228,0.5566093,0.000003741125,0.005295959,0.000001419104,0.00004915682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9087549,0.00008151153,0.09065484,0.00008772459,0.00003118861,0.0002699154,0.00002074757,0.000009825081,0.00008937701],"genre_scores_gemma":[0.9994109,0.00001318937,0.0002942785,0.00001364748,0.00002355666,0.000009669629,0.0002175506,0.000007125015,0.00001006889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09065603,"threshold_uncertainty_score":0.7833764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2218612818589492,"score_gpt":0.3821569302834472,"score_spread":0.1602956484244979,"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."}}