{"id":"W7098138691","doi":"","title":"1 DEVELOPMENT PATTERNS IN CANADA’S LARGEST URBAN AGGLOMERATION: FOUR DECADES OF EVOLUTION","year":2015,"lang":"en","type":"article","venue":"","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multispectral Scanner; Urban planning; Distribution (mathematics); Urban agglomeration; Urban area; Period (music)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005454916,0.0003388265,0.000311414,0.005277603,0.00272596,0.002602122,0.00111362,0.0004132905,0.002814076],"category_scores_gemma":[0.001556635,0.0002870231,0.0005110915,0.009819924,0.001223932,0.0007150819,0.001213452,0.000526833,0.0003521736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03728988,"about_ca_system_score_gemma":0.03472601,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9943842,"about_ca_topic_score_gemma":0.9970752,"domain_scores_codex":[0.9994773,0.00001789387,0.0000234052,0.00009166278,0.0001777221,0.0002119642],"domain_scores_gemma":[0.9973059,0.0000775034,0.0003093458,0.00007473721,0.00183515,0.0003973928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001252263,0.00002831712,0.9431187,0.0001133518,0.0001143348,0.0003683259,0.007435299,0.000556511,0.001016532,0.00266663,0.007726398,0.03673038],"study_design_scores_gemma":[0.000001924356,0.000008283901,0.9881198,0.00003066451,0.00001554943,0.00005532957,0.00279651,0.0003250758,0.0001308285,0.00004372094,0.008462223,0.000009948685],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9710508,0.003241427,0.000205492,0.00125789,0.00002626946,0.00004085031,0.00939538,0.00006076877,0.01472107],"genre_scores_gemma":[0.9929398,0.0008183651,0.0002655339,0.00005782185,0.00000605786,0.000008471378,0.002793084,0.00001641384,0.003094472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03728988,"threshold_uncertainty_score":0.2705582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02017644450448891,"score_gpt":0.1960373592055824,"score_spread":0.1758609147010935,"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."}}