{"id":"W2990188785","doi":"10.1002/jwmg.21795","title":"Habitat, Climate, and Fisher and Marten Distributions","year":2019,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Ministère des Ressources naturelles et des Forêts; Gouvernement du Québec; Université du Québec à Montréal; Université du Québec en Abitibi-Témiscamingue","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Marten; Habitat; Wildlife; Ecology; Geography; Range (aeronautics); Population; Abundance (ecology); Biology; Demography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002494995,0.0001083976,0.00009724477,0.0005414454,0.000522603,0.0004411455,0.000260213,0.0001142874,0.002708549],"category_scores_gemma":[0.000658716,0.00006991629,0.0001891717,0.0006551638,0.0002786535,0.000211966,0.0002318546,0.0001454853,0.0001475077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002178874,"about_ca_system_score_gemma":0.001006553,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.7792793,"about_ca_topic_score_gemma":0.9316867,"domain_scores_codex":[0.9998965,0.00001457961,0.000005154853,0.00002519243,0.00002338217,0.00003523066],"domain_scores_gemma":[0.9995124,0.0000661684,0.0001689308,0.00002068459,0.0001141461,0.0001175731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000169541,0.000007374613,0.9976184,0.00000437728,0.00002173441,0.00002672927,0.0001036587,0.0001650668,0.0001849161,0.00002738053,0.000205714,0.001617657],"study_design_scores_gemma":[3.801797e-7,0.000003165128,0.9993895,0.000002703051,0.000002868687,0.00001056367,0.0001693087,0.0002019694,0.0000165694,0.000005257793,0.0001964268,0.000001296168],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979764,0.000124502,0.00007500116,0.0000473183,0.000001919118,0.000004456728,0.0009724416,0.0000036244,0.0007944311],"genre_scores_gemma":[0.9989824,0.00005656163,0.0001005631,0.0000123422,0.000001829511,0.000003283082,0.0004470843,0.000001480275,0.0003944784],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7792793,"threshold_uncertainty_score":0.4440411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009419131024270642,"score_gpt":0.2164895994279429,"score_spread":0.2070704684036722,"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."}}