{"id":"W2995585781","doi":"10.1186/s13059-019-1899-5","title":"TRITEX: chromosome-scale sequence assembly of Triticeae genomes with open-source tools","year":2019,"lang":"en","type":"article","venue":"Genome biology","topic":"Chromosomal and Genetic Variations","field":"Agricultural and Biological Sciences","cited_by":287,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Biotechnology and Biological Sciences Research Council; German Network for Bioinformatics Infrastructure; Genome Prairie; Bundesministerium für Ernährung und Landwirtschaft; Bundesministerium für Bildung und Forschung; Genome Canada","keywords":"Biology; Triticeae; Computational biology; Human genetics; Sequence assembly; Genome; Chromosome; Sequence (biology); Genetics; Evolutionary biology; Whole genome sequencing; Scale (ratio); Gene; Transcriptome","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001903739,0.0001636505,0.000366666,0.00001769407,0.0001086891,0.0000632162,0.0006823837,0.0001371977,0.001793748],"category_scores_gemma":[0.0000183414,0.00006373172,0.00007118119,0.0003768164,0.0001087295,0.0001294208,0.0002327859,0.00009300179,0.0002187239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002668507,"about_ca_system_score_gemma":0.00003585153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002130075,"about_ca_topic_score_gemma":0.0002926166,"domain_scores_codex":[0.9987486,0.0001009153,0.0002886598,0.0004215961,0.00009915727,0.0003410533],"domain_scores_gemma":[0.9992743,0.0002243379,0.0001529271,0.0001403551,0.0001128621,0.0000952542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004770244,0.00009075503,0.01334061,0.000008631077,0.00002638318,0.000001228958,0.00008814209,0.00001534478,0.9707783,0.0005805466,0.000007624415,0.01501478],"study_design_scores_gemma":[0.001926804,0.005668696,0.537197,0.00004933388,0.00006928787,0.0001122018,0.0006796238,0.00006097649,0.02494758,0.0014449,0.4269506,0.0008929906],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933963,0.0004991516,0.00001208951,0.0007525997,0.00008694061,0.0005444848,0.0002704235,0.00003565397,0.004402344],"genre_scores_gemma":[0.9971764,0.00008651528,0.0006099409,0.0002368514,0.000151546,0.0000330828,0.0002550373,0.000001976853,0.001448649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9458307,"threshold_uncertainty_score":0.9991187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03408458621839151,"score_gpt":0.2481847410433481,"score_spread":0.2141001548249566,"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."}}