{"id":"W2903342910","doi":"10.1111/eva.12741","title":"Seascape genomics of eastern oyster (<i>Crassostrea virginica</i>) along the Atlantic coast of Canada","year":2018,"lang":"en","type":"article","venue":"Evolutionary Applications","topic":"Marine Bivalve and Aquaculture Studies","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; University of Toronto; Fisheries and Oceans Canada","funders":"Research and Development; Science and Engineering Research Council; Fisheries and Oceans Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Genetic diversity; Genetic variation; Eastern oyster; Population genomics; Genetic structure; Ecology; Oyster; Genetic variability; Population; Crassostrea; Genomics; Evolutionary biology; Genotype; Genetics; Genome","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":[0.00008659701,0.0000861294,0.0001099694,0.000008945842,0.0002221608,0.000002902863,0.0002777821,0.00002360689,0.0003828875],"category_scores_gemma":[0.00001246663,0.00006147863,0.00004697136,0.0001805725,0.0004225654,0.00004395841,0.0002440828,0.00005490045,0.00006499371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007676925,"about_ca_system_score_gemma":0.00007284776,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1744279,"about_ca_topic_score_gemma":0.4938791,"domain_scores_codex":[0.9992141,0.00002557693,0.0002344851,0.0001658671,0.0002163339,0.000143694],"domain_scores_gemma":[0.9993921,0.00006664095,0.0001351968,0.0003260971,0.00003763809,0.00004231811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001065707,0.00007688439,0.9443197,0.00001157147,0.00005255836,2.418055e-7,0.0002189962,0.0001145197,0.002586514,0.001438543,0.0505176,0.0006522422],"study_design_scores_gemma":[0.00009868178,0.00002399325,0.8474894,0.000006171273,0.00003390239,0.000005085733,0.0002530798,0.0003846138,0.0002925102,0.000344343,0.150985,0.00008330498],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969589,0.0001451346,0.001567276,0.002147906,0.0000623837,0.0005145787,0.00009671211,0.00001582461,0.02586119],"genre_scores_gemma":[0.9981705,0.00002176257,0.0004411307,0.0001222179,0.0001005052,0.00005948297,0.00001897,0.000005962729,0.001059474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3194512,"threshold_uncertainty_score":0.8310696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006708488250320441,"score_gpt":0.2093398991902368,"score_spread":0.2026314109399163,"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."}}