{"id":"W3134999310","doi":"10.1111/mec.15879","title":"Using seasonal genomic changes to understand historical adaptation to new environments: Parallel selection on stickleback in highly‐variable estuaries","year":2021,"lang":"en","type":"article","venue":"Molecular Ecology","topic":"Genetic diversity and population structure","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill Genome Centre; McGill University","funders":"Center for Sponsored Coastal Ocean Research; Natural Sciences and Engineering Research Council of Canada; National Oceanic and Atmospheric Administration; Royal Society; Directorate for Biological Sciences; Canada Research Chairs; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Stickleback; Gasterosteus; Biology; Parallel evolution; Natural selection; Ecology; Estuary; Adaptation (eye); Selection (genetic algorithm); Habitat; Evolutionary biology; Phylogenetics; Gene; Fishery; Genetics","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":[0.000170625,0.0001648935,0.0001987311,0.0005744345,0.0002736635,0.0003716374,0.0001793656,0.000225088,0.000632267],"category_scores_gemma":[0.0002896898,0.0001097679,0.0001790042,0.0005438223,0.0003536398,0.0002103583,0.0002982535,0.0002068201,0.00006605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002275208,"about_ca_system_score_gemma":0.0001162067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003917059,"about_ca_topic_score_gemma":0.01212977,"domain_scores_codex":[0.9998993,0.00001466053,0.000006623514,0.00005217778,0.00001277169,0.00001440667],"domain_scores_gemma":[0.9997538,0.00004391876,0.000113542,0.00002208437,0.00002751313,0.00003913949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002287362,0.00004932168,0.8061161,0.00005548978,0.0002118341,0.0001920565,0.001033789,0.0006423504,0.1773753,0.0001969549,0.00008107233,0.01381694],"study_design_scores_gemma":[0.000001320302,0.00002854082,0.9984268,0.000002382559,0.00001410608,0.00003666287,0.0001792702,0.0003557738,0.0007962212,0.00004777719,0.0001078289,0.00000339896],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994252,0.00007170688,0.0002954507,0.000007233502,0.00000112822,0.000001485788,0.00006845921,0.000003774835,0.0001256518],"genre_scores_gemma":[0.9993958,0.00004358415,0.0002952483,0.00001792412,0.000001696553,0.000004083719,0.0001268473,0.00000252612,0.0001122431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003917059,"threshold_uncertainty_score":0.007788539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02799915207812082,"score_gpt":0.2368230502797934,"score_spread":0.2088238982016726,"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."}}