{"id":"W2886345360","doi":"10.3390/su10082764","title":"Terrestrial Vertebrate Biodiversity Loss under Future Global Land Use Change Scenarios","year":2018,"lang":"en","type":"article","venue":"Sustainability","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsches Zentrum für integrative Biodiversitätsforschung Halle-Jena-Leipzig","keywords":"Biodiversity; Deforestation (computer science); Climate change; Geography; Land use; Land use, land-use change and forestry; Habitat destruction; Global change; Ecology; Extinction (optical mineralogy); Global biodiversity; Phylogenetic diversity; Arable land; Agroforestry; Environmental resource management; Environmental science; Biology; Agriculture; Phylogenetic tree","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":[0.0007265318,0.0004525207,0.0001624553,0.0006816793,0.0003294924,0.00102556,0.0003803502,0.0004148797,0.002288791],"category_scores_gemma":[0.001546246,0.0001847333,0.0008453445,0.001299627,0.000425183,0.001219849,0.00100144,0.0005217942,0.0002670132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001227638,"about_ca_system_score_gemma":0.000551724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01630851,"about_ca_topic_score_gemma":0.01392763,"domain_scores_codex":[0.9996738,0.00009576926,0.00002148696,0.0000718115,0.00006362437,0.00007352519],"domain_scores_gemma":[0.9996123,0.0000643221,0.0001492311,0.00005494326,0.00007940258,0.0000399874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003043102,0.00005885701,0.7244752,0.0002894749,0.0009215223,0.0006089029,0.0006111045,0.2103212,0.004771681,0.00771969,0.005217258,0.04470076],"study_design_scores_gemma":[0.00003696438,0.0002099284,0.8426807,0.0001269081,0.0003750799,0.0008569538,0.001997261,0.1232294,0.002298435,0.0131092,0.01500977,0.00006954995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9839181,0.0005798236,0.004299743,0.0007485106,0.00004059505,0.00001857305,0.003895973,0.0001453014,0.006353488],"genre_scores_gemma":[0.9970112,0.0002991622,0.0008958418,0.00009462702,0.000007384449,0.00001733487,0.001407942,0.00001425362,0.0002522928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01630851,"threshold_uncertainty_score":0.03242719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03066374735890781,"score_gpt":0.2645182764061053,"score_spread":0.2338545290471975,"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."}}