{"id":"W2888006456","doi":"10.1111/geb.12758","title":"Global drivers of population density in terrestrial vertebrates","year":2018,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Environment Research Council; Sight Research UK; European Research Council; McGill University","keywords":"Intraspecific competition; Ecology; Biology; Range (aeronautics); Population density; Population; Temperate climate; Density dependence; Productivity; Arid; Vertebrate; Taxon; Environmental change; Climate change","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0008951349,0.0002528341,0.0002835136,0.0008260813,0.000172734,0.0006969586,0.0002672152,0.0002581868,0.00209881],"category_scores_gemma":[0.002340666,0.0002399137,0.0006176847,0.0007332154,0.0005226526,0.0006078224,0.0006403924,0.0003170445,0.0001678482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003221544,"about_ca_system_score_gemma":0.0001163888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005875835,"about_ca_topic_score_gemma":0.005570173,"domain_scores_codex":[0.9997256,0.00008862079,0.00001496706,0.0001182079,0.00002424158,0.0000283316],"domain_scores_gemma":[0.998771,0.0005326049,0.0003835719,0.0001237755,0.0001321915,0.00005681151],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002181562,0.000006538553,0.9849012,0.00003556287,0.0002266646,0.00003833685,0.000114106,0.007920952,0.0008817157,0.000517787,0.0001559212,0.005179273],"study_design_scores_gemma":[0.000002806382,0.00001960708,0.9815695,0.00001657463,0.00004201987,0.00005799854,0.0001787089,0.01665572,0.0001054995,0.001107559,0.000236698,0.000007285223],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959435,0.0003676957,0.002294953,0.000120403,0.000004367847,0.000004850838,0.0004901307,0.00003399674,0.000740184],"genre_scores_gemma":[0.9994399,0.00005499978,0.0002412437,0.00000716211,0.000003310464,0.000002786933,0.0001953366,0.000002896112,0.00005236497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005875835,"threshold_uncertainty_score":0.01168323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004717565057987448,"score_gpt":0.2111025901531373,"score_spread":0.2063850250951499,"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."}}