{"id":"W2503331083","doi":"10.1111/mec.13795","title":"Investigating genomic and phenotypic parallelism between piscivorous and planktivorous lake trout (<i>Salvelinus namaycush</i>) ecotypes by means of<scp>RAD</scp>seq and morphometrics analyses","year":2016,"lang":"en","type":"article","venue":"Molecular Ecology","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi; Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Biology; Ecotype; Morphometrics; Salvelinus; Parallel evolution; Evolutionary biology; Ecology; Sympatric speciation; Coregonus; Reproductive isolation; Genetic divergence; Sympatry; Trout; Zoology; Genetic diversity; Phylogenetic tree; Genetics; Population; Fishery; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001538135,0.0002287446,0.0003564386,0.0001357737,0.0001147802,0.00002620584,0.000146211,0.0003230746,0.000009530558],"category_scores_gemma":[0.0002767293,0.0001963716,0.00005233508,0.0001219603,0.0003326614,0.00000792913,0.000242038,0.00009124645,0.000003003434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009923002,"about_ca_system_score_gemma":0.0000415761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004290804,"about_ca_topic_score_gemma":0.0002159901,"domain_scores_codex":[0.9986874,0.0001353939,0.0002847231,0.0004828449,0.0001143795,0.0002952171],"domain_scores_gemma":[0.9992371,0.0001221841,0.0001898162,0.0002194,0.00006657925,0.000164945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001096627,0.00001599995,0.5097737,0.00004083294,0.0002401124,0.000008907775,0.0002409526,0.00005088308,0.4855326,0.00008568366,0.0006290061,0.003370337],"study_design_scores_gemma":[0.002462458,0.0006634782,0.9096429,0.00001987925,0.0003482344,0.00009595004,0.0002690263,0.00004667624,0.07379054,0.001727851,0.01059653,0.0003364613],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992906,0.004227698,0.002108381,0.000169003,0.00006641998,0.0001665207,0.0002253275,0.00001239867,0.0001182901],"genre_scores_gemma":[0.9960594,0.0004997457,0.002810102,0.0002584337,0.00005265909,0.000005169373,0.000119168,0.00002186095,0.0001734164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4117421,"threshold_uncertainty_score":0.8007802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01595597380376554,"score_gpt":0.2425816938069952,"score_spread":0.2266257200032297,"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."}}