{"id":"W1918570603","doi":"10.1111/evo.12139","title":"LANDSCAPE GENOMICS IN ATLANTIC SALMON ( <i>SALMO SALAR</i> ): SEARCHING FOR GENE-ENVIRONMENT INTERACTIONS DRIVING LOCAL ADAPTATION","year":2013,"lang":"en","type":"article","venue":"Evolution","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Université Laval","funders":"Natural Resources Canada","keywords":"Salmo; Biology; Local adaptation; Adaptation (eye); Genomics; Population genomics; Evolutionary biology; Genetic divergence; Genetic variation; Genome; Genetics; Gene; Genetic diversity; Population; Fishery; Fish <Actinopterygii>","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001793288,0.0001121759,0.0001708071,0.0004641387,0.0002064559,0.0003161767,0.0001330587,0.0001612163,0.0005210812],"category_scores_gemma":[0.0002872071,0.00006835582,0.0001671057,0.0004777168,0.0002325908,0.0002213531,0.0002759276,0.0001930905,0.00006954518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002299994,"about_ca_system_score_gemma":0.0002207941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004317411,"about_ca_topic_score_gemma":0.01016251,"domain_scores_codex":[0.9999382,0.00001547251,0.000003292605,0.0000239924,0.000007972737,0.00001094609],"domain_scores_gemma":[0.9998355,0.00004842839,0.00006265162,0.000008426538,0.00002089524,0.00002404296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001665912,0.00007423056,0.6432778,0.00007787847,0.0001579546,0.0001399831,0.0003842844,0.001212268,0.3271204,0.0004375961,0.0001148066,0.02683624],"study_design_scores_gemma":[0.000003976254,0.00004662745,0.9969452,0.000003578893,0.00002359112,0.00007026831,0.0001352344,0.001273502,0.001131546,0.0002270044,0.0001344602,0.000005071825],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989236,0.0001058781,0.0007127534,0.0000214987,4.926262e-7,0.00000201292,0.0000573564,0.000005645061,0.00017082],"genre_scores_gemma":[0.9988269,0.00007253021,0.0008344224,0.00002519103,0.000002393357,0.000004354303,0.000153983,0.000003479316,0.00007684937],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004317411,"threshold_uncertainty_score":0.008584559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0100418123076387,"score_gpt":0.2189667477845241,"score_spread":0.2089249354768854,"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."}}