{"id":"W3198255919","doi":"10.1101/2021.09.07.459340","title":"Atria: An Ultra-fast and Accurate Trimmer for Adapter and Quality Trimming","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island; Canadian Food Inspection Agency","funders":"Canadian Food Inspection Agency","keywords":"Trimming; Computer science; Adapter (computing); Byte; Leverage (statistics); Algorithm; Executable; Parallel computing; Computer hardware; Operating system; 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":[0.002249335,0.001512653,0.0009981784,0.001691908,0.0008251168,0.001300306,0.003601588,0.001236352,0.01586154],"category_scores_gemma":[0.004984549,0.001255117,0.001104305,0.001339268,0.0005369774,0.001338482,0.002121454,0.002490233,0.01470108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005260204,"about_ca_system_score_gemma":0.0007911096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009656785,"about_ca_topic_score_gemma":0.001048528,"domain_scores_codex":[0.9980987,0.0002182163,0.0001622006,0.000550579,0.0007806135,0.000189682],"domain_scores_gemma":[0.9979791,0.0005109214,0.0003145053,0.0005885454,0.0004781198,0.0001287965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001788055,0.0003181328,0.004707735,0.001153651,0.0003771797,0.0005354902,0.0003804818,0.01830507,0.3814653,0.006780031,0.1225914,0.4615975],"study_design_scores_gemma":[0.0003011414,0.0003428264,0.005470976,0.0001581349,0.0001349789,0.001291506,0.00008656031,0.3150238,0.5527385,0.007308743,0.1169172,0.0002255152],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02086967,0.0005792775,0.7833534,0.0002045981,0.0001755888,0.0002552926,0.003882189,0.1874568,0.003223153],"genre_scores_gemma":[0.05318888,0.000224238,0.915024,0.0002601455,0.00007241873,0.0004800696,0.01109056,0.01540106,0.004258616],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01586154,"threshold_uncertainty_score":0.05306214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03021369532457625,"score_gpt":0.2623799722379642,"score_spread":0.232166276913388,"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."}}