{"id":"W4393396100","doi":"10.1016/j.cities.2024.104962","title":"Urban density and the urban forest: How well are cities balancing them in the context of climate change?","year":2024,"lang":"en","type":"article","venue":"Cities","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Social Sciences and Humanities Research Council of Canada; University of British Columbia","keywords":"Urban planning; Environmental planning; Context (archaeology); Climate change; Urban forestry; Business; Green infrastructure; Urban forest; Urban climate; Politics; Environmental resource management; Urban density; Geography; Political science; Forestry; Economics; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001878449,0.0004561674,0.0008197629,0.001428914,0.001384984,0.007002635,0.001010092,0.002841207,0.006366493],"category_scores_gemma":[0.006141389,0.0003601728,0.0005291205,0.003051822,0.007687648,0.006740027,0.002853303,0.001808165,0.0004225568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00285374,"about_ca_system_score_gemma":0.00338784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03965012,"about_ca_topic_score_gemma":0.08350799,"domain_scores_codex":[0.998287,0.0008614585,0.00004127314,0.0001798677,0.0002346463,0.0003956197],"domain_scores_gemma":[0.995903,0.001280737,0.001072213,0.0001698131,0.0006800012,0.0008943644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006740608,0.00059738,0.5381967,0.0008302471,0.0009273145,0.0004050334,0.01608245,0.006863703,0.001014059,0.2516137,0.02837311,0.1544222],"study_design_scores_gemma":[0.00004075755,0.0002237756,0.6728352,0.0007694736,0.0002988199,0.0002191732,0.08275446,0.003051165,0.0004166859,0.1674833,0.07172604,0.0001810929],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5894111,0.0660722,0.0053206,0.270414,0.00219178,0.00004933682,0.0008503644,0.00007024412,0.06562032],"genre_scores_gemma":[0.9874467,0.007484009,0.0003850269,0.002760667,0.0007212958,0.0000102283,0.00005768734,0.00001548267,0.001118754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03965012,"threshold_uncertainty_score":0.07883865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02476693460353909,"score_gpt":0.2211777714360271,"score_spread":0.196410836832488,"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."}}