{"id":"W2462337320","doi":"10.1016/j.margen.2016.06.003","title":"Targeted sequencing for high-resolution evolutionary analyses following genome duplication in salmonid fish: Proof of concept for key components of the insulin-like growth factor axis","year":2016,"lang":"en","type":"article","venue":"Marine Genomics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fisheries and Oceans Canada; Directorate for Biological Sciences; NERC Biomolecular Analysis Facility; Natural Environment Research Council; Karl-Franzens-Universität Graz; University of Aberdeen; School of Biological Sciences, Washington State University; University of Glasgow; Sight Research UK; Escuela de Ciencias Biológicas, Universidad Nacional Costa Rica","keywords":"Biology; Evolutionary biology; Gene duplication; Genome; Phylogenetics; Lineage (genetic); Phylogenetic tree; Genetics; Vertebrate; Gene; Computational biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007607583,0.0003668929,0.000356671,0.0003870141,0.0003223366,0.0004796556,0.0003695248,0.0004879188,0.0008799483],"category_scores_gemma":[0.0007717428,0.0003400636,0.0004937789,0.0003090556,0.0003208634,0.0003430021,0.0006227084,0.001067916,0.0006204179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000303293,"about_ca_system_score_gemma":0.0006633569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00107865,"about_ca_topic_score_gemma":0.002358489,"domain_scores_codex":[0.9995993,0.00004199246,0.0000248752,0.0001561902,0.000135819,0.00004178991],"domain_scores_gemma":[0.9996036,0.0001101153,0.0000819248,0.00004440911,0.0001012233,0.0000587539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007296383,0.0000235535,0.00161725,0.0001261646,0.00002726995,0.00008505528,0.00008602916,0.0004043454,0.9855735,0.0004984695,0.0001901805,0.01129526],"study_design_scores_gemma":[0.000152172,0.001158244,0.06562278,0.0001249407,0.0003135482,0.001593511,0.0003419488,0.03125063,0.8518407,0.00281113,0.04467607,0.000114247],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.631511,0.003128424,0.3513267,0.0007174963,0.0002338251,0.0003673843,0.006554455,0.001282127,0.004878588],"genre_scores_gemma":[0.6432144,0.003233171,0.3347526,0.0009598542,0.0001033654,0.0004615121,0.01212072,0.0005853419,0.004568961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00107865,"threshold_uncertainty_score":0.004023373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02796136114532884,"score_gpt":0.2516309055379982,"score_spread":0.2236695443926693,"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."}}