{"id":"W2341858319","doi":"10.1371/journal.pgen.1005954","title":"Chromosomal-Level Assembly of the Asian Seabass Genome Using Long Sequence Reads and Multi-layered Scaffolding","year":2016,"lang":"en","type":"article","venue":"PLoS Genetics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":219,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"National Human Genome Research Institute; Russian Academy of Sciences; National Institute of General Medical Sciences; Directorate for Biological Sciences; National Science Foundation; Siberian Branch, Russian Academy of Sciences; National Research Foundation; Biotechnology and Biological Sciences Research Council; Megagrants; Russian Foundation for Basic Research","keywords":"Biology; Synteny; Contig; Sequence assembly; Evolutionary biology; Genome; Population; Genetics; Genomics; Whole genome sequencing; Clade; Phylogenetics; Gene; Transcriptome","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":[0.0001076716,0.0002019852,0.0001993554,0.00002892167,0.0001414143,0.00001751579,0.0002866197,0.0001287048,0.000002975728],"category_scores_gemma":[0.00005832972,0.0001363756,0.00007992151,0.00008153167,0.0002120443,0.000001382192,0.0004130586,0.00004357987,0.000001332636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002383417,"about_ca_system_score_gemma":0.00009000873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006892907,"about_ca_topic_score_gemma":0.00004830407,"domain_scores_codex":[0.9988527,0.0000633602,0.0002618945,0.0003696636,0.000153285,0.0002990809],"domain_scores_gemma":[0.9991313,0.00001722748,0.0001566212,0.0004805054,0.0001345914,0.00007978455],"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":[0.000009802973,0.00003585215,0.04536676,0.00002804779,0.0001076953,0.000001380941,0.00007110598,0.00006688486,0.9529505,0.00001211593,0.000003932271,0.001345957],"study_design_scores_gemma":[0.0005004737,0.0001061598,0.05595738,0.00005564251,0.00006300589,0.00002477,0.00005398373,0.000727732,0.9420947,0.00003166153,0.0001547058,0.0002297711],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995841,0.002340755,0.001127235,0.0001419024,0.0001069775,0.0002328476,0.000124155,0.000003394599,0.00008170355],"genre_scores_gemma":[0.9937985,0.0006895815,0.00515853,0.00005534289,0.0001337416,0.00000798169,0.0000033924,0.0000353338,0.0001175849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01085576,"threshold_uncertainty_score":0.5561237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08272079234482772,"score_gpt":0.277406062823532,"score_spread":0.1946852704787043,"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."}}