{"id":"W3196174352","doi":"10.1111/gcb.15800","title":"Wealth and urbanization shape medium and large terrestrial mammal communities","year":2021,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Environment and Protected Areas; University of Alberta","funders":"","keywords":"Urbanization; Species richness; Biodiversity; Per capita; Urban ecosystem; Ecology; Ecosystem; Geography; Ecosystem services; Mammal; Wildlife; Economic geography; Biology; Demography; Sociology; Population","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001309505,0.00008453035,0.0001278033,0.00000934813,0.0001455183,0.00001427538,0.00006571535,0.0001062885,0.0006390307],"category_scores_gemma":[0.00001346088,0.00007711097,0.00001066471,0.0000996359,0.0001615481,0.00007446094,0.0002884324,0.0000705042,0.00003721667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006035839,"about_ca_system_score_gemma":0.00001619097,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004141688,"about_ca_topic_score_gemma":0.02643951,"domain_scores_codex":[0.9993098,0.0001176267,0.00009591054,0.0001581834,0.00005702679,0.0002614216],"domain_scores_gemma":[0.9997222,0.00002245669,0.00003966802,0.000108553,0.000004900448,0.0001022044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001581489,0.00001883134,0.9935638,0.000005990506,0.000003422492,0.000005562025,0.0004750658,8.90314e-9,0.00002578811,0.0007934659,0.001240912,0.003851317],"study_design_scores_gemma":[0.0006445639,0.0001937288,0.9254441,0.000007951442,0.00001032155,0.00005720803,0.0007339417,0.0002384342,0.000003459998,0.0006255705,0.07192017,0.0001205929],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942839,0.001401185,0.00005670713,0.002833924,0.0002681573,0.0001257498,0.0001173621,0.00002611148,0.000886968],"genre_scores_gemma":[0.996511,0.0008620442,0.000163313,0.002023441,0.0002464752,0.000008055988,0.0001201731,0.000003949885,0.00006157599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07067926,"threshold_uncertainty_score":0.9913254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04527284936402489,"score_gpt":0.2871425242079984,"score_spread":0.2418696748439735,"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."}}