{"id":"W4377079709","doi":"10.1109/tcbb.2023.3277733","title":"Counting Sorting Scenarios and Intermediate Genomes for the Rank Distance","year":2023,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Natural Sciences and Engineering Research Council of Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Sorting; Rank (graph theory); Genome; Biology; Computer science; Computational biology; Evolutionary biology; Mathematics; Genetics; Combinatorics; Algorithm; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003261994,0.00120635,0.001592322,0.005093599,0.002188796,0.003789525,0.00267462,0.002867381,0.01032218],"category_scores_gemma":[0.02236411,0.0005877241,0.002028825,0.004164688,0.002887693,0.01332624,0.003913253,0.003635767,0.001826605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00182978,"about_ca_system_score_gemma":0.001613988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001249006,"about_ca_topic_score_gemma":0.001163923,"domain_scores_codex":[0.9958087,0.001511027,0.0002820781,0.0008895878,0.0009929138,0.0005157545],"domain_scores_gemma":[0.9856947,0.01007608,0.00110977,0.00132967,0.001170329,0.0006193149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001475493,0.0001081937,0.002533119,0.0002392167,0.00004061636,0.0001779984,0.0002381564,0.06479635,0.00179742,0.8715253,0.00543051,0.05296557],"study_design_scores_gemma":[0.00002228185,0.00008215211,0.0004134279,0.00006493933,0.00002635621,0.0003699608,0.0001242699,0.1475982,0.001044619,0.8442963,0.005908849,0.00004855992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07346211,0.001994984,0.9009948,0.002072226,0.0001058669,0.0001924817,0.001056725,0.0005933309,0.01952744],"genre_scores_gemma":[0.4519602,0.002608456,0.5285277,0.0009728274,0.0006100433,0.000558358,0.003720934,0.0004360477,0.01060551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01032218,"threshold_uncertainty_score":0.03453112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01672981774030772,"score_gpt":0.2735063017091181,"score_spread":0.2567764839688104,"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."}}