{"id":"W3016197773","doi":"10.1534/g3.120.401184","title":"Mapping of Adaptive Traits Enabled by a High-Density Linkage Map for Lake Trout","year":2020,"lang":"en","type":"article","venue":"G3 Genes Genomes Genetics","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Natural Resources and Forestry; Université Laval","funders":"National Science Foundation","keywords":"Trout; Linkage (software); Geography; Biology; Fishery; Ecology; Fish <Actinopterygii>; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001249629,0.0001854446,0.0003004876,0.00002384388,0.0001672414,0.000009331999,0.0002747954,0.00009117947,0.0004167402],"category_scores_gemma":[0.000006156292,0.0001882109,0.00008451557,0.0001514476,0.0001776563,0.00004110887,0.0003746868,0.00006619198,0.0001324795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003242027,"about_ca_system_score_gemma":0.000008888981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001178328,"about_ca_topic_score_gemma":0.0005708266,"domain_scores_codex":[0.9987948,0.0000392532,0.0002757416,0.0003946967,0.0001521908,0.0003433161],"domain_scores_gemma":[0.9995478,0.00005395657,0.000141156,0.0001484731,0.0000184595,0.00009009313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003528543,0.0004049583,0.1250184,0.0005710948,0.0009607585,0.00002123485,0.01144842,0.009774289,0.09034656,0.000346627,0.2487129,0.5120419],"study_design_scores_gemma":[0.001670809,0.0008536146,0.1130176,0.00001008057,0.0001592869,0.000001030284,0.001458898,0.002351689,0.01404704,0.001032899,0.8647975,0.0005996284],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8412142,0.06337629,0.07086968,0.01016641,0.0007973342,0.004229926,0.0008189792,0.0002568964,0.008270267],"genre_scores_gemma":[0.9655059,0.008705497,0.02056,0.002953569,0.000153459,0.0001003061,0.0000558336,0.00003673762,0.001928694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6160845,"threshold_uncertainty_score":0.7675016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01780008480076584,"score_gpt":0.2000978616680579,"score_spread":0.1822977768672921,"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."}}