{"id":"W2305317181","doi":"10.31357/fesympo.v20i0.2583","title":"Urban Growth and Climate Change Strategies for Effective Mitigation and Adaptation","year":2015,"lang":"en","type":"article","venue":"Proceedings of International Forestry and Environment Symposium","topic":"Science and Climate Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto and Region Conservation Authority","funders":"","keywords":"Urbanization; Climate change; Population; Urban climate; Population growth; Flood myth; Consumption (sociology); Business; Environmental planning; Urban planning; Natural resource economics; Urban resilience; Geography; Economic growth; Economics; Civil engineering; Engineering","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":[0.0006972964,0.0007328035,0.0002557981,0.0008074497,0.00206565,0.003834166,0.0009306906,0.001290502,0.0115678],"category_scores_gemma":[0.000921654,0.0001387704,0.0002627539,0.001406896,0.003042992,0.00204392,0.004698765,0.001734948,0.001302922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009754883,"about_ca_system_score_gemma":0.01558456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1537636,"about_ca_topic_score_gemma":0.3281972,"domain_scores_codex":[0.9993033,0.0001629139,0.00001041936,0.0000399128,0.0002510334,0.00023243],"domain_scores_gemma":[0.9996412,0.00003223729,0.00002769755,0.0000248512,0.0001697525,0.0001044067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001761885,0.00008046178,0.003220573,0.000537709,0.00002547591,0.0002128694,0.004354563,0.01100789,0.0007912603,0.6035329,0.1858118,0.1904069],"study_design_scores_gemma":[0.000004645934,0.00002775751,0.004417476,0.0003904534,0.000009299,0.00004998768,0.00834836,0.002002017,0.0003093806,0.05337078,0.9310544,0.00001546627],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02033615,0.03415577,0.01834402,0.2166158,0.001507499,0.0002978349,0.001045981,0.0003848352,0.707312],"genre_scores_gemma":[0.6938934,0.08262154,0.020288,0.009297777,0.0008841226,0.0003471886,0.00117076,0.000203127,0.1912942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1537636,"threshold_uncertainty_score":0.3057371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01816318271887664,"score_gpt":0.2341842843664645,"score_spread":0.2160211016475878,"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."}}