{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001362721,0.0001440199,0.0001554763,0.0006797671,0.0005574955,0.000419311,0.0002151352,0.00011633,0.0008840992],"category_scores_gemma":[0.0002511935,0.00007367237,0.0001820822,0.001014103,0.0002811996,0.0000834128,0.0003246165,0.0001612297,0.00007838345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001284829,"about_ca_system_score_gemma":0.001315579,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.630187,"about_ca_topic_score_gemma":0.8259937,"domain_scores_codex":[0.9998964,0.000006995271,0.000004860859,0.00004581955,0.00001962246,0.00002621988],"domain_scores_gemma":[0.9997393,0.00001974215,0.00005623281,0.00001271271,0.0001182504,0.00005373771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001517201,0.00002586308,0.9346294,0.00003449173,0.0001582017,0.0001066405,0.00132592,0.0005217997,0.05130891,0.0001620019,0.0002454439,0.01132954],"study_design_scores_gemma":[6.322038e-7,0.000004751692,0.9994067,0.000002717158,0.000006995987,0.000009361442,0.0001903319,0.00009344936,0.0001401746,0.0000061528,0.00013735,0.000001259988],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991701,0.00003960416,0.0001100876,0.000008667723,6.565107e-7,0.000001966536,0.0002718176,0.000002442918,0.0003947782],"genre_scores_gemma":[0.9989385,0.00003470589,0.0001479989,0.00001043248,4.36946e-7,0.000002187797,0.0004163976,0.000002760849,0.0004464236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.369813,"threshold_uncertainty_score":0.7439818,"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."}}