{"id":"W3110399804","doi":"10.3390/su12239966","title":"Subcentres as Destinations: Job Decentralization, Polycentricity, and the Sustainability of Commuting Patterns in Canadian Metropolitan Areas, 1996–2016","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Polycentricity; Metropolitan area; Sustainability; Economic geography; Public transport; Destinations; Geography; Sustainable transport; Microdata (statistics); Sustainable city; Downtown; Sustainable development; Global city; Business; Census; Transport engineering; Population; Political science; Engineering; Corporate governance","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":[0.0009221797,0.0003946695,0.0003400184,0.002479169,0.003564764,0.001988202,0.001340221,0.0003678437,0.002282361],"category_scores_gemma":[0.002598922,0.0002682063,0.0005770546,0.006201204,0.001459492,0.0007679456,0.002357484,0.000694988,0.0002348642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03454765,"about_ca_system_score_gemma":0.04089465,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9963853,"about_ca_topic_score_gemma":0.9988797,"domain_scores_codex":[0.9990621,0.00006046294,0.00003794978,0.0001531785,0.0002803954,0.0004059253],"domain_scores_gemma":[0.9977926,0.00008596326,0.0003292952,0.0001277925,0.001112307,0.0005520993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001152079,0.00003028318,0.954222,0.00009105264,0.00006262637,0.0001022207,0.0126561,0.0006408545,0.0002816579,0.001405562,0.00475155,0.02564106],"study_design_scores_gemma":[0.000001882274,0.000005787791,0.9885139,0.00003038558,0.000009612739,0.0000158474,0.006598213,0.0002411983,0.00004144195,0.00004769052,0.004482713,0.00001130279],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862786,0.001068388,0.0002073419,0.0006317887,0.00002285362,0.00004420992,0.00488491,0.00001782386,0.006844144],"genre_scores_gemma":[0.9954602,0.0004270237,0.000205485,0.00003822637,0.000005845512,0.00001502533,0.001627581,0.000007216427,0.002213339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03454765,"threshold_uncertainty_score":0.2506618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01027933643658102,"score_gpt":0.2910175060499777,"score_spread":0.2807381696133967,"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."}}