{"id":"W2965745078","doi":"10.1016/j.jenvman.2019.109299","title":"Conservation Reserve Program is a key element for managing white-tailed deer populations at multiple spatial scales","year":2019,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Conservation Association; Natural Sciences and Engineering Research Council of Canada; U.S. Fish and Wildlife Service; Canada Research Chairs; U.S. Department of the Interior","keywords":"Geography; Spatial ecology; Abundance (ecology); Habitat; Recreation; Scale (ratio); Wildlife; Ecology; Spatial variability; Temporal scales; Metapopulation; Population; Range (aeronautics); Environmental resource management; Environmental science; Cartography; Biological dispersal; Biology; Demography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002184094,0.0002246231,0.0003536351,0.000948226,0.001835427,0.001574768,0.001429315,0.0004958567,0.00363042],"category_scores_gemma":[0.002215971,0.0002128512,0.0002533488,0.0007854615,0.0005124674,0.001530729,0.001398186,0.0005836622,0.0004873205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009674242,"about_ca_system_score_gemma":0.003881819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02345701,"about_ca_topic_score_gemma":0.09941387,"domain_scores_codex":[0.9992936,0.0002841921,0.00004095808,0.0001071257,0.0001378479,0.0001363178],"domain_scores_gemma":[0.9978968,0.0002470802,0.0006108808,0.0002065031,0.0003534694,0.0006852922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009032271,0.00111307,0.387646,0.0005057369,0.000644348,0.0005986286,0.002129082,0.007508865,0.00929562,0.006188393,0.01908169,0.5651982],"study_design_scores_gemma":[0.00004216883,0.00106175,0.8241751,0.0006578282,0.0003003699,0.0005487794,0.01253576,0.008495808,0.001614064,0.00469995,0.1457798,0.00008861868],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8947281,0.003788328,0.03479725,0.01467937,0.0003380633,0.001162733,0.0009210415,0.0007093382,0.04887579],"genre_scores_gemma":[0.968424,0.001437257,0.02238164,0.0008092197,0.000119256,0.0004920145,0.0004407084,0.00003726062,0.005858568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02345701,"threshold_uncertainty_score":0.04664099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01384465190048599,"score_gpt":0.2361204586077437,"score_spread":0.2222758067072577,"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."}}