{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001267403,0.0001374982,0.0001114714,0.0000425385,0.00007074678,0.00005360029,0.0001065876,0.0000778548,0.000008060942],"category_scores_gemma":[0.000005770366,0.0001116853,0.00002881436,0.00007339955,0.00003948029,0.000009531796,0.0000520753,0.00005431332,0.00001924912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008622177,"about_ca_system_score_gemma":0.00003998547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002114096,"about_ca_topic_score_gemma":0.0000120568,"domain_scores_codex":[0.9993229,0.000007224119,0.0001955216,0.0001239975,0.0001187065,0.00023168],"domain_scores_gemma":[0.9995493,0.000003095534,0.00008590897,0.0002367,0.0000434313,0.00008152412],"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":[0.002771274,0.001073963,0.0797179,0.002588003,0.001382925,0.00004739951,0.01112395,0.009583924,0.7777722,0.004896125,0.005452507,0.1035898],"study_design_scores_gemma":[0.03659746,0.01426664,0.2512967,0.0003850485,0.0007347377,0.001566125,0.008488043,0.1088021,0.2450098,0.000958173,0.3247843,0.007110921],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9192895,0.0006457091,0.06163371,0.0001717425,0.00005609232,0.0005356185,0.00001239666,0.00005166525,0.01760354],"genre_scores_gemma":[0.6640418,0.0002712796,0.3327975,0.0006950293,0.0002648741,0.00001454165,0.0002437849,0.00003294749,0.001638226],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5327624,"threshold_uncertainty_score":0.4554393,"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."}}