{"id":"W7099293481","doi":"","title":"www.mdpi.com/journal/ijerph Nature Appropriation and Associations with Population Health in Canada’s Largest Cities","year":2013,"lang":"en","type":"article","venue":"","topic":"Knee injuries and reconstruction techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Appropriation; Population; Population health; Sustainability; Deforestation (computer science); Consumption (sociology); Natural resource; Biodiversity","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":[0.0002575716,0.0002192344,0.0002510929,0.001918028,0.0009907485,0.001283917,0.0005846499,0.0005142463,0.02696936],"category_scores_gemma":[0.001677257,0.000134751,0.0003124787,0.003152948,0.0006225399,0.0003213906,0.000546694,0.000278363,0.001231904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005596508,"about_ca_system_score_gemma":0.01362256,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9164062,"about_ca_topic_score_gemma":0.958137,"domain_scores_codex":[0.9997625,0.00001734667,0.00001432262,0.00002974252,0.00009124164,0.00008482655],"domain_scores_gemma":[0.9990321,0.0001764073,0.000254226,0.00003744016,0.0002376302,0.0002621679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005094674,0.00007465603,0.9652995,0.000175704,0.0000677615,0.0001132909,0.0004996944,0.0001228923,0.0001122655,0.0003611655,0.007964468,0.02515765],"study_design_scores_gemma":[0.000006490302,0.00001762848,0.9924701,0.0001929129,0.00004287548,0.0001503111,0.001668529,0.0002879928,0.0001260922,0.0001960099,0.004830376,0.00001058134],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9283256,0.007422836,0.000364444,0.004602966,0.0001350028,0.00007263558,0.0221112,0.00005714879,0.0369082],"genre_scores_gemma":[0.9864727,0.00321252,0.0004092599,0.0003769603,0.00005887177,0.00002608095,0.002344717,0.00001447203,0.007084338],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08359385,"threshold_uncertainty_score":0.1681722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004378370797786784,"score_gpt":0.2330432449501402,"score_spread":0.2286648741523535,"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."}}