{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001290753,0.00007137575,0.0001131685,0.00002681225,0.00008543825,0.000003926536,0.00007529183,0.0001601249,0.0001422168],"category_scores_gemma":[0.00002970037,0.00006855914,0.00003328275,0.000440891,0.0005364332,0.0001002534,0.00007294805,0.00003107069,0.00002840511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004604756,"about_ca_system_score_gemma":0.00000645008,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003250481,"about_ca_topic_score_gemma":0.03759337,"domain_scores_codex":[0.9993832,0.00006553912,0.0001452393,0.0001920771,0.00005569566,0.0001582304],"domain_scores_gemma":[0.9998052,0.00001779072,0.00006438739,0.00007249774,0.000004266769,0.00003582541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009109115,0.0000478283,0.997587,7.954864e-7,0.00000979315,0.000002332237,0.00001302309,0.000003613271,0.00001860401,0.0003758765,0.0005892643,0.001260794],"study_design_scores_gemma":[0.0004585059,0.0001918617,0.989682,0.000002123212,0.00001194508,0.000005340014,0.0000192831,0.0001242678,0.00001296871,0.009294385,0.0001333301,0.00006395482],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980944,0.000009193148,0.000009077664,0.0002680991,0.0002893062,0.00009812164,0.00001427039,0.000012967,0.001204611],"genre_scores_gemma":[0.9993648,0.000008386877,0.0001878004,0.0003921127,0.0000242733,0.000002506998,0.00001750364,8.154811e-7,0.000001797103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03434289,"threshold_uncertainty_score":0.979968,"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."}}