{"id":"W2135744524","doi":"10.1002/ece3.1303","title":"The<scp>PREDICTS</scp>database: a global database of how local terrestrial biodiversity responds to human impacts","year":2014,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":253,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dillon Consulting; Université du Québec à Montréal; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Yukon Department of Environment; Canadian Forest Service; Thompson Rivers University; University of Alberta; Natural Resources Canada; Carleton University","funders":"Biotechnology and Biological Sciences Research Council; Natural Environment Research Council; Sight Research UK","keywords":"Biodiversity; Database; Biome; Geography; Ecology; Global biodiversity; Habitat; Range (aeronautics); Taxonomic rank; Ecosystem; Biology; Taxon","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001248955,0.00195882,0.001392033,0.005971898,0.0004657151,0.002467803,0.002620385,0.001712951,0.02380717],"category_scores_gemma":[0.006905754,0.0007945589,0.001028529,0.01442266,0.0004754025,0.002687064,0.002357891,0.001409667,0.01758196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001047855,"about_ca_system_score_gemma":0.002321564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03076538,"about_ca_topic_score_gemma":0.0256281,"domain_scores_codex":[0.9988593,0.0001523706,0.0002419129,0.0003054609,0.0003224796,0.0001185046],"domain_scores_gemma":[0.9934082,0.001897394,0.001325938,0.001461022,0.001146834,0.0007605636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002668559,0.00006562514,0.02808194,0.003752481,0.0003190888,0.000219722,0.0002068689,0.007043955,0.001206822,0.002488814,0.9334387,0.02290919],"study_design_scores_gemma":[0.0002685202,0.0000791527,0.1075008,0.0007538006,0.0001850057,0.0002899983,0.0003938912,0.01066502,0.001870191,0.003427641,0.8744195,0.0001463762],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001359278,0.00008036763,0.0004731906,0.00005129141,0.000008959627,0.00001556369,0.9963368,0.0009341717,0.0007402853],"genre_scores_gemma":[0.003285017,0.0001048256,0.001191472,0.00003055155,0.000006093824,0.00007350215,0.9949595,0.0001406437,0.0002084531],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03076538,"threshold_uncertainty_score":0.07964289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02681029047643447,"score_gpt":0.2224826901862845,"score_spread":0.1956723997098501,"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."}}