{"id":"W1619623942","doi":"10.1111/j.1365-294x.2004.02396.x","title":"FAST‐TRACK: Integrating QTL mapping and genome scans towards the characterization of candidate loci under parallel selection in the lake whitefish (<i>Coregonus clupeaformis</i>)","year":2004,"lang":"en","type":"article","venue":"Molecular Ecology","topic":"Genetic Mapping and Diversity in Plants and Animals","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5078,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Biology; Coregonus clupeaformis; Quantitative trait locus; Local adaptation; Genetics; Coregonus; Selection (genetic algorithm); Sympatric speciation; Directional selection; Evolutionary biology; Candidate gene; Genome; Natural selection; Genetic variation; Gene; Population; Machine learning","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":[0.001072412,0.0004099153,0.0004477271,0.0008400345,0.0002715033,0.0004884381,0.0004560776,0.000292714,0.001353854],"category_scores_gemma":[0.001106069,0.0003074305,0.0004371825,0.0007356381,0.0003625187,0.0004834339,0.0008340842,0.0003994607,0.0001891504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003117557,"about_ca_system_score_gemma":0.0003465892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004128673,"about_ca_topic_score_gemma":0.01358208,"domain_scores_codex":[0.9997165,0.00003961913,0.00001025853,0.0001165768,0.00007359539,0.00004341102],"domain_scores_gemma":[0.999428,0.0002406214,0.0001206411,0.00008519487,0.00005458475,0.00007091621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001355866,0.0001938156,0.07720963,0.0002203632,0.0004175451,0.0002812788,0.0007435026,0.007150137,0.7881258,0.001412565,0.0008802256,0.1220094],"study_design_scores_gemma":[0.0008036738,0.001838216,0.7443782,0.00004433187,0.0006325367,0.002117129,0.0005249533,0.09472565,0.1417725,0.003339042,0.009614878,0.0002088771],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9593467,0.0002019773,0.03709564,0.00005704434,0.00001329316,0.00006158918,0.001528461,0.0009132536,0.0007821379],"genre_scores_gemma":[0.8550234,0.0001915005,0.1391042,0.0001025168,0.00001893268,0.0001392889,0.003390581,0.0002380119,0.001791548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004128673,"threshold_uncertainty_score":0.008209288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009071951951945599,"score_gpt":0.2053243528870435,"score_spread":0.1962524009350979,"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."}}