{"id":"W2999194397","doi":"10.1016/j.gecco.2020.e00920","title":"Linking habitat, predators and alternative prey to explain recruitment variations of an endangered caribou population","year":2020,"lang":"en","type":"article","venue":"Global Ecology and Conservation","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université du Québec à Rimouski","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Ministère des Forêts, de la Faune et des Parcs; Canada Foundation for Innovation; Université du Québec à Rimouski","keywords":"Ursus; Ecology; Predation; Woodland caribou; Interspecific competition; Habitat; Threatened species; Population; Endangered species; Biology; Abundance (ecology); Apex predator; Habitat destruction; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002022435,0.0000963303,0.000139057,0.00001929039,0.0001610919,0.0000140092,0.00007223862,0.0001154037,0.00006802666],"category_scores_gemma":[0.0001580887,0.0001049759,0.00001383731,0.0001854943,0.00008134564,0.0003381179,0.00009265196,0.00005973102,0.00001108948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009220929,"about_ca_system_score_gemma":0.00001888846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007163157,"about_ca_topic_score_gemma":0.007150604,"domain_scores_codex":[0.9991013,0.0001576793,0.0002376014,0.0002861204,0.00008695449,0.000130297],"domain_scores_gemma":[0.999572,0.00007875158,0.0001367332,0.00008230832,0.00002271878,0.0001074847],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008701118,0.00003463715,0.9907862,0.000007154838,0.00001638698,0.000001360795,0.001032273,0.0004324303,0.0001736164,0.003144169,0.0001984344,0.004086321],"study_design_scores_gemma":[0.0003507842,0.0003083904,0.9853342,0.000005876821,0.0000272597,0.000004045685,0.0001110628,0.008752708,0.0000770804,0.004610625,0.0003248873,0.00009303615],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9899785,0.00001000663,0.001414061,0.007699927,0.00009859318,0.0005443388,0.00005078019,0.00002712911,0.0001766867],"genre_scores_gemma":[0.9924449,0.00000701916,0.002849261,0.004445877,0.00002605757,0.00008145162,0.0001339614,0.000003660545,0.000007841011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008320278,"threshold_uncertainty_score":0.4280791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03027392346867449,"score_gpt":0.2629787690562461,"score_spread":0.2327048455875716,"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."}}