{"id":"W2805514251","doi":"10.29173/spectrum35","title":"Auto Sequencer: A DNA Sequence Alignment and Assembly Tool","year":2018,"lang":"en","type":"article","venue":"Spectrum","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Sequence assembly; DNA sequencing; Sequence (biology); Merge (version control); DNA; Sequencing by ligation; DNA sequencer; Computer science; DNA nanoball sequencing; Alignment-free sequence analysis; Software; Hybrid genome assembly; Computational biology; Sequence analysis; Sequence logo; Consensus sequence; Reference genome; Sequence alignment; Algorithm; Biology; Genetics; Base sequence; Information retrieval; Programming language; Peptide sequence; Genomic library; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00009553579,0.0001221535,0.00009783387,0.00001679753,0.0001116965,0.00002532458,0.00010675,0.00006065862,0.00002173291],"category_scores_gemma":[0.0000148423,0.0001125938,0.00003357534,0.00004038765,0.0001561483,6.39592e-7,0.0001442217,0.00003159189,0.00002389023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001584189,"about_ca_system_score_gemma":0.00004664284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003929855,"about_ca_topic_score_gemma":0.00009456143,"domain_scores_codex":[0.9992453,0.00001747095,0.0001127341,0.0003110112,0.00007482067,0.0002386853],"domain_scores_gemma":[0.9996383,0.000004116804,0.00003682381,0.0002440855,0.00002543333,0.00005129343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001235458,0.00001039212,0.003622825,0.000004406399,0.00004032494,0.000002538465,0.00006720096,0.000001051607,0.9932581,0.0009385967,0.0007433131,0.001298952],"study_design_scores_gemma":[0.0003441664,0.0008153399,0.02671005,0.00001027983,0.00002117764,0.00003568782,0.00006779576,0.00006726808,0.8830218,0.0027334,0.08585839,0.000314624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9941463,0.0006724485,0.0002068724,0.0006436684,0.0001820899,0.0001154503,0.00001810005,0.00000586628,0.004009221],"genre_scores_gemma":[0.9976349,0.0002933355,0.0006839951,0.0003758435,0.0003470451,0.00001315125,0.000006303384,0.00001256992,0.0006328012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1102362,"threshold_uncertainty_score":0.4591442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01416994902413205,"score_gpt":0.2510992418464547,"score_spread":0.2369292928223227,"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."}}