{"id":"W2611036498","doi":"10.1093/bioinformatics/btx281","title":"Assembling draft genomes using contiBAIT","year":2017,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia; BC Cancer Agency","funders":"Canadian Institutes of Health Research; Terry Fox Foundation; Terry Fox Research Institute; Michael Smith Health Research BC; Canadian Cancer Society; National Institutes of Health","keywords":"Bioconductor; Computer science; Sequence assembly; Orientation (vector space); Massively parallel; Data mining; Computational biology; Biology; Parallel computing; Genetics; Mathematics; 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":[0.0001285037,0.0001267365,0.0001321716,0.00002470966,0.0004666022,0.000144433,0.0003132778,0.00008535841,0.000004737949],"category_scores_gemma":[0.00006649275,0.0001170946,0.00007297775,0.00001340493,0.00008641697,0.000002732398,0.0002834498,0.00004095706,0.00001543531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008991634,"about_ca_system_score_gemma":0.00005105928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001273505,"about_ca_topic_score_gemma":0.00001305379,"domain_scores_codex":[0.9993708,0.000006367174,0.0002137616,0.0001110902,0.00008335862,0.0002146114],"domain_scores_gemma":[0.9991189,0.000004902397,0.0001981424,0.0005499712,0.00007076207,0.00005726912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006392932,0.00007794884,0.1145677,0.000170019,0.0005263578,0.00000606238,0.001028214,0.0007460114,0.8387629,0.0009423474,0.002887262,0.04022126],"study_design_scores_gemma":[0.003489008,0.0006343003,0.07943289,0.0001009805,0.0002332542,0.0001081669,0.001769711,0.05874419,0.2654115,0.0007065734,0.5872959,0.002073473],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986883,0.0009128759,0.002259193,0.00006502266,0.0003352678,0.0001274915,0.00002106027,0.000004345642,0.009391675],"genre_scores_gemma":[0.9789855,0.0003656288,0.01993167,0.0001466676,0.0002381371,0.000002974941,0.00001210386,0.00001385179,0.0003034277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5844087,"threshold_uncertainty_score":0.4774981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03405659169511104,"score_gpt":0.2855712378516305,"score_spread":0.2515146461565194,"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."}}