{"id":"W2793822153","doi":"10.1111/eva.12627","title":"Quantifying functional connectivity: The role of breeding habitat, abundance, and landscape features on range‐wide gene flow in sage‐grouse","year":2018,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"U.S. Bureau of Land Management; U.S. Geological Survey; Natural Sciences and Engineering Research Council of Canada; U.S. Fish and Wildlife Service","keywords":"Habitat; Abundance (ecology); Ecology; Biology; Grouse; Range (aeronautics); Landscape connectivity; Biological dispersal; Population","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":[0.0008015641,0.0002477615,0.0001941263,0.000986292,0.0002081026,0.0005987721,0.0002905088,0.0002305375,0.0007104033],"category_scores_gemma":[0.002604731,0.0001232432,0.0003033089,0.0006035748,0.0003961894,0.0006395878,0.0004912311,0.000201548,0.00006777103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001960768,"about_ca_system_score_gemma":0.0001184351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004692367,"about_ca_topic_score_gemma":0.01272802,"domain_scores_codex":[0.9997149,0.000135522,0.00001544278,0.00007516607,0.00002863034,0.00003028752],"domain_scores_gemma":[0.9985853,0.0008384903,0.0002587884,0.0001242079,0.00007930195,0.0001139168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006494376,0.00001601042,0.986249,0.00001336873,0.0001928696,0.00002726024,0.0001838241,0.00408347,0.005169117,0.0001898138,0.00004391697,0.003766246],"study_design_scores_gemma":[0.000001975058,0.00004406651,0.977185,0.000004221222,0.00002644181,0.00005465422,0.0001068149,0.02194335,0.0003338508,0.0002334761,0.00005981825,0.000006290527],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991762,0.00001575356,0.0006385655,0.000006082082,3.057605e-7,0.000001054506,0.00004629491,0.000004533335,0.0001111066],"genre_scores_gemma":[0.9995881,0.000004695273,0.0003158498,0.00000290881,7.303863e-7,0.000001447564,0.00005705505,0.000001666424,0.00002758591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004692367,"threshold_uncertainty_score":0.009330153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0111424842120728,"score_gpt":0.2180099716274751,"score_spread":0.2068674874154023,"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."}}