{"id":"W4382631507","doi":"10.1093/bioinformatics/btad250","title":"PlasBin-flow: a flow-based MILP algorithm for plasmid contigs binning","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; European Commission; Genome Canada","keywords":"Contig; Plasmid; Genetics; Biology; Computer science; Computational biology; Sequence assembly; Genome; Algorithm; DNA; 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":[0.0002018279,0.0001976712,0.0001948674,0.00008382099,0.0001757872,0.0000404953,0.0001874878,0.0001409013,0.00000552887],"category_scores_gemma":[0.00007362856,0.000186063,0.0001465894,0.000143298,0.00006395663,0.000001578548,0.0001110472,0.00004457902,0.00004966875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000122882,"about_ca_system_score_gemma":0.00009556128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002242024,"about_ca_topic_score_gemma":0.000007787774,"domain_scores_codex":[0.9989673,0.000009965991,0.0003362431,0.0001773656,0.000124647,0.0003844123],"domain_scores_gemma":[0.9993841,0.00004591505,0.0001050997,0.0002739675,0.0001064988,0.00008440494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002872671,0.0001828956,0.002209181,0.0007024041,0.0009439777,0.00001040295,0.001769659,0.01696019,0.2446734,0.0001395202,0.2997715,0.4323497],"study_design_scores_gemma":[0.001288253,0.000285793,0.0004179613,0.00001920148,0.00002877021,0.000003148667,0.0002256034,0.6279889,0.03097052,0.00003846771,0.3384374,0.0002959156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5553096,0.0007595489,0.4329059,0.0006826477,0.002137315,0.001829551,0.003439259,0.0001344593,0.00280175],"genre_scores_gemma":[0.1045119,0.0007428611,0.882064,0.002909646,0.001151053,0.0005392864,0.005040462,0.0001587178,0.002882135],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6110288,"threshold_uncertainty_score":0.758743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611820032584436,"score_gpt":0.2409062830554634,"score_spread":0.224788082729619,"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."}}