{"id":"W2110720895","doi":"10.2980/21-2-3687","title":"Fine-scale winter resource selection by American martens in boreal forests and the effect of snow depth on access to coarse woody debris","year":2014,"lang":"en","type":"article","venue":"Ecoscience","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Ontario Forest Research Institute; Canadian Forest Service","funders":"Natural Sciences and Engineering Research Council of Canada; Johnson and Johnson; Canadian Natural Resources Limited","keywords":"Snow; Taiga; Boreal; Coarse woody debris; Habitat; Ecology; Environmental science; Range (aeronautics); Home range; Larch; Geography; Physical geography; Biology; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004367523,0.00008044753,0.0001596707,0.00001623362,0.0002114739,0.00002942426,0.0002635476,0.00002757138,0.00001012797],"category_scores_gemma":[0.0002073822,0.00002604014,0.0000292038,0.0003887823,0.0004763122,0.00006836077,0.0001410971,0.00007311909,0.00000739899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001299005,"about_ca_system_score_gemma":0.000001472016,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009321254,"about_ca_topic_score_gemma":0.04264222,"domain_scores_codex":[0.9992514,0.0001396657,0.00008770592,0.0002277063,0.0001033466,0.000190136],"domain_scores_gemma":[0.9993008,0.000547315,0.00005649426,0.00003536076,0.00001486381,0.00004510074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002185019,0.00001978575,0.9579958,0.000001663286,0.000002190038,3.045701e-7,0.0000768248,0.00001037707,0.0004031598,0.000008655262,0.007991856,0.03327092],"study_design_scores_gemma":[0.0002310308,0.001477257,0.9925297,0.00001005913,0.00000474221,0.000001692853,0.00004698819,0.000389537,0.00319155,0.00002471205,0.002029608,0.00006305453],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957387,0.000005767929,0.000006884901,0.00332765,0.00003629747,0.0001899822,0.00001047933,0.00001088934,0.0006733444],"genre_scores_gemma":[0.9993511,0.000003546468,0.00001312284,0.0005120933,0.00002147449,0.00001027195,0.000003346973,2.750293e-7,0.00008470169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0417101,"threshold_uncertainty_score":0.9748271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007227606301185015,"score_gpt":0.2164052955553867,"score_spread":0.2091776892542017,"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."}}