{"id":"W3208708402","doi":"10.32920/ryerson.14636007.v1","title":"Urban resilience in Canada : research priorities and best practices for climate resilience in cities.","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Resilience (materials science); Urban resilience; Environmental planning; Environmental resource management; Climate resilience; Best practice; Geography; Climate change; Political science; Urban planning; Environmental science; Engineering; Civil engineering; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004911356,0.0007370467,0.000775687,0.003555301,0.008097601,0.005250596,0.001972819,0.001561161,0.005179295],"category_scores_gemma":[0.01075618,0.0003800133,0.0008691732,0.009677699,0.002792589,0.002550818,0.004822931,0.002089847,0.0003209821],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.1327912,"about_ca_system_score_gemma":0.3664926,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9973788,"about_ca_topic_score_gemma":0.9986429,"domain_scores_codex":[0.9975701,0.0002996587,0.00008495819,0.0001729501,0.000947409,0.000924984],"domain_scores_gemma":[0.9886379,0.001201268,0.0004727627,0.0002367671,0.006712354,0.002739134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000227668,0.0001358978,0.08033504,0.004207003,0.0003214198,0.0003765583,0.0146968,0.00743717,0.0006674212,0.08671188,0.3997002,0.4051829],"study_design_scores_gemma":[0.00006717507,0.00006259501,0.4326933,0.01208307,0.0007286784,0.0001656092,0.06536119,0.006065048,0.002273319,0.03713604,0.4430616,0.0003022208],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1413615,0.2679082,0.01003157,0.4339878,0.002186894,0.0005170057,0.03685248,0.0005646386,0.10659],"genre_scores_gemma":[0.8239668,0.1134028,0.02058613,0.008161593,0.0002542739,0.0003482882,0.008754195,0.0001797763,0.02434613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8672088,"threshold_uncertainty_score":0.9634722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05451297682280426,"score_gpt":0.3207358146242177,"score_spread":0.2662228378014134,"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."}}