{"id":"W2145757848","doi":"10.1186/1471-2105-10-s1-s2","title":"Genome aliquoting with double cut and join","year":2009,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Royal Society; Royal Society of Canada","keywords":"Genome; Sign (mathematics); Heuristic; Combinatorics; Genetics; Biology; Gene; Human genome; Computational biology; Mathematics; Computer science; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002199779,0.000978049,0.001262846,0.001115212,0.00105284,0.001422312,0.003097639,0.001657825,0.007674374],"category_scores_gemma":[0.005850224,0.0005242951,0.001276481,0.001650552,0.001440548,0.00237022,0.002752103,0.001881181,0.001153405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001157043,"about_ca_system_score_gemma":0.001142129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001558985,"about_ca_topic_score_gemma":0.001299523,"domain_scores_codex":[0.9986143,0.0003290247,0.00008647759,0.0004191459,0.0004217205,0.0001293269],"domain_scores_gemma":[0.9953892,0.002919621,0.0003994041,0.0007880869,0.0003022567,0.00020139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001030069,0.000345728,0.003037727,0.0005669731,0.0001170394,0.0003280493,0.00065336,0.5532245,0.02047751,0.07046112,0.005618352,0.3441396],"study_design_scores_gemma":[0.0001542037,0.0003151129,0.0005368114,0.00004307138,0.00006322913,0.0002907775,0.0001882792,0.8811033,0.01993737,0.08840463,0.008926275,0.00003695266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04851251,0.000344754,0.9455979,0.0002697312,0.00004327099,0.0002216682,0.0002787458,0.001479449,0.003251982],"genre_scores_gemma":[0.1779371,0.0001438088,0.817453,0.0001097128,0.00003692527,0.0002539433,0.0009434429,0.0003357179,0.00278626],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007674374,"threshold_uncertainty_score":0.02567333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01280339597768899,"score_gpt":0.2268005749068623,"score_spread":0.2139971789291733,"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."}}