{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003651558,0.00008371013,0.0001254115,0.000008281656,0.0006478591,0.00001916784,0.0001308883,0.00007724707,0.000006790853],"category_scores_gemma":[0.0003030316,0.00003244341,0.00003250809,0.0001363631,0.0002091159,0.00008813081,0.0001682221,0.00006441232,0.00001645692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003207377,"about_ca_system_score_gemma":0.000006386634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004054218,"about_ca_topic_score_gemma":0.01681842,"domain_scores_codex":[0.999294,0.0001104845,0.00009200742,0.0001747284,0.00008977987,0.0002390402],"domain_scores_gemma":[0.9994996,0.0002977518,0.00006066521,0.00002715382,0.00002468525,0.0000901876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002084558,0.00007202482,0.7888685,0.00000767685,0.00003218976,0.000004854135,0.0000488448,0.000001343222,0.133587,0.001777789,0.07361083,0.001780511],"study_design_scores_gemma":[0.0002150347,0.0006411813,0.9817838,0.000008939616,0.00001583373,0.000004965519,0.0002658783,0.00003337736,0.0002318512,0.0002272029,0.01654036,0.00003156191],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977336,0.0001454231,0.00002368528,0.001270337,0.0001080297,0.00009306742,0.0003831412,0.00002216723,0.0002205412],"genre_scores_gemma":[0.999524,0.00004898863,0.00001064693,0.00008768061,0.00018388,0.000002708575,0.00008247812,1.636058e-7,0.00005948343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1929154,"threshold_uncertainty_score":0.938507,"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."}}