{"id":"W2100291334","doi":"10.1002/jwmg.471","title":"Do trappers understand marten habitat?","year":2012,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; University of Guelph; Canadian Forest Service","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Natural Resources","keywords":"Marten; Habitat; Land cover; Geography; Ecology; Environmental science; Forest management; Basal area; Physical geography; Land use; Forestry; Biology","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":[0.0004432628,0.0001827497,0.0001499693,0.0003711718,0.0002652232,0.0009770977,0.0003171654,0.0002555662,0.002349826],"category_scores_gemma":[0.002920614,0.0001539364,0.0001211668,0.0003785825,0.0004011964,0.0008478553,0.0002539222,0.0001781928,0.0002015513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005820132,"about_ca_system_score_gemma":0.0003678058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1149062,"about_ca_topic_score_gemma":0.2543408,"domain_scores_codex":[0.9998804,0.00002979927,0.000004434647,0.00003177278,0.00002850221,0.00002502675],"domain_scores_gemma":[0.9989963,0.0003112376,0.0004156508,0.0000850606,0.00008468286,0.0001070536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003430379,0.00001866515,0.986625,0.00002553336,0.00003764671,0.00006475468,0.002037938,0.0006495973,0.0003719589,0.0002385819,0.0004886453,0.009407448],"study_design_scores_gemma":[0.000002635396,0.00001764304,0.9916906,0.00002207857,0.00001472111,0.0001092,0.003371247,0.003143932,0.00008155797,0.0003948188,0.001144417,0.000006968426],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962416,0.00020919,0.0005953996,0.0002377007,0.000003807008,0.000005916458,0.0002086916,0.000010684,0.002486989],"genre_scores_gemma":[0.9990705,0.0001274209,0.0002921113,0.00003685876,0.000003226306,0.000001680061,0.00009464683,0.000001927193,0.0003717512],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1149062,"threshold_uncertainty_score":0.2284747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01715899268821613,"score_gpt":0.2291133610273665,"score_spread":0.2119543683391504,"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."}}