{"id":"W2901368943","doi":"10.1111/mec.14942","title":"Putatively adaptive genetic variation in the giant California sea cucumber (<i>Parastichopus californicus</i>) as revealed by environmental association analysis of restriction‐site associated DNA sequencing data","year":2018,"lang":"en","type":"article","venue":"Molecular Ecology","topic":"Coral and Marine Ecosystems Studies","field":"Environmental Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Fisheries and Oceans Canada; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Alaska Department of Fish and Game","keywords":"Biology; Local adaptation; Ecology; Context (archaeology); Population; Adaptation (eye); Environmental gradient; Natural selection; Single-nucleotide polymorphism; Candidate gene; Genetic variation; Invertebrate; Spatial ecology; Evolutionary biology; Genetics; Habitat; Genotype; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008111474,0.0001604411,0.0003379217,0.00009512884,0.0001609443,0.00001567353,0.0003110521,0.0001257131,0.0003361308],"category_scores_gemma":[0.0004438075,0.0001357051,0.00006069252,0.0008298131,0.0001049114,0.0001001924,0.0003928289,0.0001416048,0.0002582764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001213694,"about_ca_system_score_gemma":0.00002618373,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.008571376,"about_ca_topic_score_gemma":0.02167724,"domain_scores_codex":[0.9977706,0.0006346878,0.0004414208,0.0004557044,0.0003760853,0.0003215005],"domain_scores_gemma":[0.9989254,0.0002307158,0.000452796,0.0003276582,0.00002081852,0.00004257077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002920899,0.000139976,0.9571305,0.00000241761,0.000501471,0.00002434219,0.0005957084,0.001039245,0.03832675,0.000007781561,0.002067084,0.0001354985],"study_design_scores_gemma":[0.0003518326,0.0001947079,0.9503024,0.000003002844,0.00069976,0.000003314309,0.0001217213,0.04734111,0.0002093576,0.0002227196,0.0004055275,0.0001445989],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957725,0.00003040155,0.001787832,0.000286589,0.00007963134,0.0003470896,0.001101097,0.00001289086,0.0005819279],"genre_scores_gemma":[0.9987323,0.00002387339,0.0002295123,0.0003170294,0.00001715575,0.00004028624,0.0005161511,0.00001028246,0.0001133543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04630186,"threshold_uncertainty_score":0.9980307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01021069041760444,"score_gpt":0.2244841662209792,"score_spread":0.2142734758033747,"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."}}