{"id":"W6927499983","doi":"10.3389/fevo.2020.00001.s001","title":"Data_Sheet_1_Cumulative Effects and Boreal Woodland Caribou: How Bow-Tie Risk Analysis Addresses a Critical Issue in Canada's Forested Landscapes.pdf","year":2020,"lang":"en","type":"dataset","venue":"Figshare","topic":"Legionella and Acanthamoeba research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Woodland caribou; Boreal; Risk assessment; Taiga; Cumulative effects; Predation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002967509,0.000795921,0.0006753706,0.004694135,0.001863205,0.003585725,0.002816719,0.001036775,0.1326992],"category_scores_gemma":[0.0169499,0.0007876325,0.0007751232,0.01030096,0.0005624861,0.001424932,0.001122877,0.001134542,0.02004148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01481968,"about_ca_system_score_gemma":0.03618246,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.949698,"about_ca_topic_score_gemma":0.9719489,"domain_scores_codex":[0.9982451,0.00009700136,0.0001368403,0.00009579441,0.001214782,0.000210466],"domain_scores_gemma":[0.9784192,0.003101595,0.0007248418,0.001149892,0.01598709,0.0006174358],"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":[0.00007314752,0.00003598633,0.009960597,0.0007567902,0.0000300644,0.00008726029,0.0001864207,0.002850496,0.000182861,0.002946943,0.9439364,0.03895305],"study_design_scores_gemma":[0.00008240603,0.00001449711,0.03008855,0.0009669975,0.0000386673,0.00007480563,0.0004223976,0.001746591,0.0005240802,0.002124294,0.963836,0.00008080375],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009023628,0.0001929159,0.001082452,0.000633938,0.00005734487,0.000232434,0.9738297,0.0008126983,0.02225618],"genre_scores_gemma":[0.01901297,0.002088146,0.01761513,0.0007306534,0.00007693213,0.0008780502,0.91487,0.001140625,0.04358746],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1326992,"threshold_uncertainty_score":0.4439232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01588553518310473,"score_gpt":0.2806516420432038,"score_spread":0.264766106860099,"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."}}