{"id":"W2888977297","doi":"10.1007/s10878-018-0346-y","title":"An approximation algorithm for genome sorting by reversals to recover all adjacencies","year":2018,"lang":"en","type":"article","venue":"Journal of Combinatorial Optimization","topic":"Genome Rearrangement Algorithms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Shandong Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Georgia Southern University; Office of the Vice President for Research and Economic Development, University at Buffalo; National Science Foundation","keywords":"Genome; Sorting; Approximation algorithm; Biology; Genomics; Gene; Computational biology; Mathematics; Algorithm; Computer science; Genetics","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":[0.001317002,0.001696297,0.002161915,0.001625039,0.001269117,0.0020146,0.003921678,0.002295987,0.01095103],"category_scores_gemma":[0.005273939,0.0008182505,0.001824092,0.00286352,0.001223127,0.003069542,0.002385561,0.003068468,0.00206342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002216245,"about_ca_system_score_gemma":0.004754857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007377039,"about_ca_topic_score_gemma":0.008987012,"domain_scores_codex":[0.9990616,0.0001859528,0.00005157206,0.0002518505,0.000228745,0.0002202133],"domain_scores_gemma":[0.9976996,0.001199976,0.0001418899,0.0005760724,0.0002267975,0.0001556404],"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":[0.001174183,0.000740889,0.001656809,0.000428236,0.0001358733,0.0001538642,0.0002850554,0.3753553,0.009467131,0.06223328,0.02626468,0.5221047],"study_design_scores_gemma":[0.0003267324,0.0002055266,0.0004299041,0.00003778633,0.00007337593,0.0001560034,0.0001332847,0.9185097,0.003851637,0.07133289,0.004908884,0.00003431872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06620978,0.0005110825,0.9175353,0.0009991091,0.0002303155,0.0003702978,0.0007159078,0.003559385,0.009868822],"genre_scores_gemma":[0.190192,0.0002753331,0.7988266,0.0003765572,0.00009144312,0.0003990325,0.001776912,0.0004706813,0.007591298],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01095103,"threshold_uncertainty_score":0.0366348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01089987414633301,"score_gpt":0.2672347587110028,"score_spread":0.2563348845646697,"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."}}