{"id":"W7053080517","doi":"","title":"Sprawl and Smart Growth in Greater Vancouver: A Comparison of Vancouver, British Columbia, with Seattle, Washington","year":2002,"lang":"en","type":"article","venue":"Issue Lab (Candid)","topic":"Plasma Diagnostics and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Urban sprawl; Smart growth; Census; Growth management; Population growth","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":[0.0003225201,0.0003121959,0.000323352,0.003010282,0.003324829,0.002627659,0.0008268227,0.0003628453,0.004275899],"category_scores_gemma":[0.001471329,0.0002409264,0.000227544,0.00893784,0.0007189743,0.0004763932,0.001253207,0.0006488428,0.0004773799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01859956,"about_ca_system_score_gemma":0.01717776,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9935209,"about_ca_topic_score_gemma":0.9986041,"domain_scores_codex":[0.9994294,0.00003401424,0.00003204283,0.00006129592,0.0001956817,0.0002475686],"domain_scores_gemma":[0.9976507,0.0001133411,0.0001870334,0.00005123978,0.001399297,0.0005983939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003473656,0.0001337649,0.8984816,0.0002293434,0.0001401106,0.001050936,0.008846703,0.000388816,0.0007017368,0.0008869707,0.0165785,0.0722141],"study_design_scores_gemma":[0.000008298891,0.00001878808,0.980072,0.00005394466,0.00002194754,0.00008755639,0.01170367,0.0001110666,0.00008217674,0.0000351131,0.007791283,0.00001412215],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9700361,0.00228609,0.00008626148,0.0009685625,0.00003708794,0.00005279313,0.004886766,0.00003093891,0.02161539],"genre_scores_gemma":[0.986093,0.001842295,0.000139325,0.0001709672,0.000008414665,0.0000297423,0.00240376,0.00001709713,0.009295423],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01859956,"threshold_uncertainty_score":0.1349499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007345672894676829,"score_gpt":0.1909388798405229,"score_spread":0.183593206945846,"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."}}