{"id":"W2551900470","doi":"10.1111/eva.12443","title":"Environmental and anthropogenic drivers of connectivity patterns: A basis for prioritizing conservation efforts for threatened populations","year":2016,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Association of Petroleum Producers; Alberta Conservation Association; ConocoPhillips; Shell; Ministry of Forests, Lands and Natural Resource Operations; Parks Canada; World Wildlife Fund","keywords":"Threatened species; Biological dispersal; Landscape connectivity; Ecology; Habitat fragmentation; Biology; Habitat; Population; Habitat destruction; Gene flow; Resistance (ecology); Predation; Geography; Genetic variation; Demography","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":[0.001091213,0.0003184434,0.0003174531,0.001124798,0.0009619521,0.001831926,0.000683746,0.000442482,0.00142371],"category_scores_gemma":[0.004086107,0.0001891497,0.0001944244,0.001061973,0.001285103,0.001142188,0.001029281,0.0007051408,0.00005283521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002047489,"about_ca_system_score_gemma":0.003447491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07842475,"about_ca_topic_score_gemma":0.2980381,"domain_scores_codex":[0.999613,0.0001608536,0.00001722096,0.00006660159,0.00007372215,0.00006854191],"domain_scores_gemma":[0.9987929,0.0003421007,0.0004702432,0.00005230493,0.0001609949,0.0001814772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007123529,0.00005061175,0.8870274,0.00033736,0.0002421782,0.0003708012,0.003147694,0.01958196,0.004499524,0.00989197,0.001008591,0.0737707],"study_design_scores_gemma":[0.00001058043,0.0001097994,0.9585756,0.0002079251,0.0001358496,0.0003012478,0.007551279,0.01603136,0.0003683791,0.01170022,0.004948381,0.0000594358],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9800519,0.001865568,0.007363529,0.004394759,0.00002230819,0.00005776782,0.0003547101,0.00005703151,0.005832502],"genre_scores_gemma":[0.9961449,0.0006392199,0.002844081,0.00007531646,0.00001074079,0.00001345346,0.00009326659,0.00000550046,0.0001735259],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07842475,"threshold_uncertainty_score":0.1559365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643972503778279,"score_gpt":0.249322008576381,"score_spread":0.2328822835385982,"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."}}