{"id":"W2909968513","doi":"10.1002/ecm.1355","title":"Spatially structured statistical network models for landscape genetics","year":2019,"lang":"en","type":"article","venue":"Ecological Monographs","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Landscape connectivity; Biological dispersal; Ecology; Computer science; Statistical model; Data mining; Machine learning; Biology; Population","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":[0.004215398,0.001175006,0.001128074,0.002802863,0.0006792772,0.002008875,0.002563626,0.002008827,0.01023431],"category_scores_gemma":[0.01540085,0.0006365512,0.001783812,0.002724126,0.001520216,0.003032123,0.00163625,0.002540529,0.001543549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002899957,"about_ca_system_score_gemma":0.001085654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0157979,"about_ca_topic_score_gemma":0.01434291,"domain_scores_codex":[0.9986583,0.000838406,0.00004667031,0.00023125,0.0001481401,0.00007727784],"domain_scores_gemma":[0.9905348,0.007295186,0.0008461167,0.0004020653,0.0006768418,0.0002449148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001647127,0.00001979764,0.001289154,0.00004725127,0.00005644137,0.00005682614,0.00008513181,0.6263157,0.000143707,0.3622639,0.003211798,0.006493897],"study_design_scores_gemma":[0.000006228224,0.000004550765,0.0001715402,0.00001218843,0.000006396612,0.00001165996,0.00001054245,0.8455363,0.00001656417,0.1524676,0.001749033,0.000007496734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01659567,0.00083035,0.9720131,0.001859437,0.0001213585,0.00008939329,0.001716777,0.000558966,0.006214899],"genre_scores_gemma":[0.6056839,0.004215434,0.3384515,0.001105874,0.0007413739,0.001864991,0.005741087,0.0009281981,0.04126759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0157979,"threshold_uncertainty_score":0.03423721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01214265925582211,"score_gpt":0.2178228651061201,"score_spread":0.2056802058502979,"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."}}