{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001910039,0.0001252624,0.0001757549,0.00001585903,0.0001290163,0.00001973958,0.0001906651,0.0001944433,0.00563164],"category_scores_gemma":[0.00003228943,0.0001021725,0.00006769772,0.0001453347,0.000107744,0.00008314186,0.00009851471,0.000115552,0.0002404427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002201864,"about_ca_system_score_gemma":0.000009724592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000530783,"about_ca_topic_score_gemma":0.0001452927,"domain_scores_codex":[0.9989323,0.00006154349,0.000204665,0.0003228757,0.0001125024,0.0003661262],"domain_scores_gemma":[0.9993656,0.000300708,0.00006746922,0.0001660422,0.000009776097,0.00009044155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007383995,0.000054638,0.8808488,0.000002889607,0.00001421987,0.00000173719,0.0000205296,0.09443519,0.00002564514,0.003733967,0.01900499,0.001783575],"study_design_scores_gemma":[0.0003900843,0.000355804,0.8163462,0.000001103067,0.00001401275,0.000001386136,0.000006605159,0.08311369,0.000005323984,0.09625022,0.003380722,0.0001348995],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9810148,0.00002997033,0.01390384,0.0002718777,0.000351816,0.0006132995,0.00002691028,0.00005531785,0.003732172],"genre_scores_gemma":[0.9723892,0.00001710535,0.0258052,0.001393256,0.00006779682,0.00007852038,0.00006238084,0.000008987196,0.0001776038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09251625,"threshold_uncertainty_score":0.9952773,"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."}}