{"id":"W2810174954","doi":"10.1111/evo.13486","title":"The demographic history of Atlantic salmon ( <i>Salmo salar</i> ) across its distribution range reconstructed from approximate Bayesian computations*","year":2018,"lang":"en","type":"article","venue":"Evolution","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Compute Canada","keywords":"Approximate Bayesian computation; Biology; Introgression; Demographic history; Salmo; Beringia; Range (aeronautics); Evolutionary biology; Gene flow; Population; Ecology; Genetic variation; Arctic; Demography; Fishery; Genetics; Fish <Actinopterygii>; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001515627,0.0001155159,0.0001090977,0.00002021035,0.000244495,0.0000149056,0.0001620133,0.0001646371,0.00002401134],"category_scores_gemma":[0.00005748602,0.0001033014,0.00008286823,0.000128075,0.0003017991,0.000008301176,0.00006387418,0.00007264141,0.000008404334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006614326,"about_ca_system_score_gemma":0.00006620271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001565186,"about_ca_topic_score_gemma":0.0002368938,"domain_scores_codex":[0.9990939,0.0001051649,0.0002237828,0.0002403983,0.0001457568,0.0001909924],"domain_scores_gemma":[0.9992618,0.0000188584,0.0001870368,0.0002482658,0.0002345653,0.00004946403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001188076,0.0001612416,0.5705937,0.0001118591,0.0004527459,0.000002879393,0.001427727,0.00048668,0.349162,0.003783076,0.05980616,0.01282388],"study_design_scores_gemma":[0.001730813,0.0003047655,0.8799334,0.00005466788,0.0001234378,0.0000266783,0.0003766658,0.007931973,0.01428515,0.002101849,0.09265236,0.0004782942],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.963704,0.001688818,0.03308798,0.0001045883,0.0007992886,0.0001780148,0.0002551156,0.00002218659,0.0001600487],"genre_scores_gemma":[0.9977909,0.00007505988,0.0004567203,0.00003750536,0.0002193993,0.000004200492,0.001245856,0.000008579172,0.000161827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3348769,"threshold_uncertainty_score":0.4212508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009490955411938694,"score_gpt":0.2200966103063992,"score_spread":0.2106056548944605,"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."}}