{"id":"W2523109915","doi":"10.1002/jwmg.21147","title":"Pronghorn resource selection and habitat fragmentation in North Dakota","year":2016,"lang":"en","type":"article","venue":"Journal of Wildlife Management","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Habitat; Geography; Wildlife; Normalized Difference Vegetation Index; Wetland; Ecology; Vegetation (pathology); Environmental science; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001417613,0.000141918,0.0001290684,0.000524183,0.0005318717,0.000381559,0.0002308078,0.0001039354,0.001445645],"category_scores_gemma":[0.0003085839,0.0001221694,0.00008748239,0.0005586303,0.000356266,0.0001991444,0.0004504815,0.0001550149,0.0001251387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001093988,"about_ca_system_score_gemma":0.0004054962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1624808,"about_ca_topic_score_gemma":0.5824573,"domain_scores_codex":[0.9999073,0.00001666431,0.000007114058,0.00003342849,0.00001639256,0.00001905156],"domain_scores_gemma":[0.9996599,0.00003587552,0.0001464068,0.00001945666,0.0000620501,0.00007618395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003571999,0.00001912252,0.9924766,0.0000218439,0.00003445792,0.00009279447,0.0006950836,0.000109036,0.002642151,0.00004734793,0.0002401861,0.003585704],"study_design_scores_gemma":[0.000001120338,0.000004679128,0.9989679,0.00000463214,0.000003265714,0.0000298315,0.0005526379,0.00007791357,0.0000418154,0.000008764149,0.0003062122,0.00000127116],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991333,0.00006192002,0.00003970337,0.00002363985,0.000001611911,0.000003400084,0.0001963096,0.000001887079,0.0005381405],"genre_scores_gemma":[0.99903,0.00008285558,0.0001503534,0.00003335068,0.000001112995,0.00001131939,0.0002551728,0.000001567397,0.0004343382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1624808,"threshold_uncertainty_score":0.3230701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006099120489516301,"score_gpt":0.2028617495026845,"score_spread":0.1967626290131682,"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."}}