{"id":"W2610924493","doi":"10.1016/j.compbiolchem.2017.03.012","title":"PECC: Correcting contigs based on paired-end read distribution","year":2017,"lang":"en","type":"article","venue":"Computational Biology and Chemistry","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; National Natural Science Foundation of China","keywords":"Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000147875,0.00009119012,0.0001031753,0.00001119797,0.0004859703,0.0001090082,0.0002719743,0.00009753702,0.00009278233],"category_scores_gemma":[0.0002553156,0.00008566699,0.00003837486,0.00002454085,0.0001485639,0.00007867641,0.00006540543,0.0001006274,0.00001873432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000187329,"about_ca_system_score_gemma":0.00004350577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004023886,"about_ca_topic_score_gemma":4.326575e-7,"domain_scores_codex":[0.9993903,0.0000270976,0.0001137183,0.0002750888,0.00007187804,0.0001218903],"domain_scores_gemma":[0.9992436,0.0002854177,0.0001197429,0.0002170215,0.0000750958,0.00005909171],"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":[0.00004944908,0.0001401945,0.01931725,0.00003698173,0.00003568816,0.00002503978,0.00004371136,0.00006426615,0.005387067,0.006994613,0.001117478,0.9667883],"study_design_scores_gemma":[0.0022482,0.0001425063,0.2542658,0.0001769318,0.00002378679,0.0001857482,0.00005552872,0.6212979,0.07204023,0.0398696,0.008878864,0.0008149478],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4592915,0.00004439857,0.5228957,0.003831684,0.0004260571,0.00008955903,0.0001397067,0.0001721704,0.01310919],"genre_scores_gemma":[0.9949884,0.000002376338,0.004195471,0.0002996828,0.00007213794,0.00000696844,0.0002804696,0.000002510697,0.0001519442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9659733,"threshold_uncertainty_score":0.373774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056220635510617,"score_gpt":0.275028273531399,"score_spread":0.2544660671762928,"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."}}