{"id":"W2112455659","doi":"10.1111/j.1752-4571.2008.00047.x","title":"Applications of graph theory to landscape genetics","year":2008,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":142,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Trent University","funders":"","keywords":"Gene flow; Biology; Population; Centrality; Landscape connectivity; Ecology; Habitat; Node (physics); Evolutionary biology; Population genetics; Gene; Biological dispersal; Genetics; Genetic variation; Demography; Statistics; Mathematics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005631088,0.00009270509,0.00008434387,0.00008144687,0.0002305085,0.000002771296,0.0002321796,0.0000930571,0.0001017244],"category_scores_gemma":[0.000008732918,0.0001024039,0.00007033168,0.0003128072,0.0001096322,0.000002551819,0.00009130768,0.0000460872,0.00006792559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005807422,"about_ca_system_score_gemma":0.00006368982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003648987,"about_ca_topic_score_gemma":0.000002933121,"domain_scores_codex":[0.9993059,0.00003025654,0.000170274,0.0002440975,0.0001284706,0.00012102],"domain_scores_gemma":[0.9992393,0.00001309697,0.00006476291,0.0004361376,0.0001531781,0.00009351262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003769618,0.001143476,0.3267901,0.0001309776,0.0004879715,0.000002339956,0.0009941504,0.02704684,0.2533324,0.1554616,0.1855402,0.04869292],"study_design_scores_gemma":[0.0002489479,0.00007710463,0.1500664,0.000002410896,0.00002839718,0.00003845366,0.0001063717,0.00002289693,0.005466954,0.006935109,0.8368015,0.0002054012],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1800775,0.002392888,0.8079004,0.0002887254,0.0000590153,0.001133342,0.0003703134,0.00003999491,0.00773785],"genre_scores_gemma":[0.9774395,0.0002353519,0.01930735,0.0002092987,0.0001857843,0.0002981295,0.0004904025,0.00001117467,0.001822966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.797362,"threshold_uncertainty_score":0.4175908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0078381095964624,"score_gpt":0.226615634206678,"score_spread":0.2187775246102156,"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."}}