{"id":"W2762757730","doi":"10.1111/ecog.02813","title":"Disentangling vegetation and climate as drivers of Australian vertebrate richness","year":2017,"lang":"en","type":"article","venue":"Ecography","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Murdoch University; James Cook University","keywords":"Species richness; Ecology; Vegetation (pathology); Body size and species richness; Geography; Productivity; Biodiversity; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.001030774,0.0002623944,0.0003482603,0.001126816,0.0004326436,0.0009443068,0.00027616,0.0002655618,0.001313093],"category_scores_gemma":[0.002678552,0.0004472613,0.000620305,0.0007733732,0.0006646997,0.0007981201,0.001137873,0.0003532623,0.0001330648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000446376,"about_ca_system_score_gemma":0.0003353607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02375978,"about_ca_topic_score_gemma":0.05067768,"domain_scores_codex":[0.9995216,0.0002383469,0.00003026342,0.00008596542,0.00005203034,0.00007180077],"domain_scores_gemma":[0.9981767,0.0009586969,0.0004183097,0.0001382897,0.0001406093,0.0001674187],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000349401,0.00001254902,0.9907409,0.00002470145,0.0001508364,0.00007660496,0.0006402177,0.001053978,0.002302259,0.0002657399,0.00002409264,0.004673156],"study_design_scores_gemma":[5.901151e-7,0.0000110851,0.9974766,0.00000320049,0.0000191066,0.00002430954,0.000119949,0.002128982,0.00005007477,0.0001022644,0.00006084086,0.000002891718],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990644,0.0001377882,0.0003690345,0.00003313593,8.144129e-7,0.00000323408,0.0000487775,0.000003041294,0.0003398002],"genre_scores_gemma":[0.9996562,0.00004320965,0.0001572392,0.000003902805,0.000001417866,0.000002322723,0.00003910243,0.000001252925,0.00009533637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02375978,"threshold_uncertainty_score":0.047243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01645972702440034,"score_gpt":0.256849534358597,"score_spread":0.2403898073341966,"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."}}