{"id":"W4247642419","doi":"10.24124/2009/bpgub605","title":"Multiple Anchor Staged Local Sequence Alignment Algorithm - MASAA.","year":2009,"lang":"fi","type":"dissertation","venue":"","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Heritage; University of Northern British Columbia; Library and Archives Canada","funders":"","keywords":"Pairwise comparison; Alignment-free sequence analysis; Smith–Waterman algorithm; Sequence (biology); Algorithm; Computer science; Multiple sequence alignment; Similarity (geometry); Tree (set theory); Sequence alignment; Computation; Biological data; Suffix tree; Data mining; Data structure; Artificial intelligence; Mathematics; Bioinformatics; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001002235,0.001125034,0.001191153,0.001077084,0.001045449,0.001091533,0.001483192,0.001042188,0.006132355],"category_scores_gemma":[0.001871891,0.0004861451,0.0008379052,0.001526561,0.0002922035,0.00114206,0.0008696603,0.001659868,0.00921068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004586284,"about_ca_system_score_gemma":0.001243011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001167316,"about_ca_topic_score_gemma":0.002023796,"domain_scores_codex":[0.9991964,0.0002004344,0.00006941245,0.0002153537,0.0002504568,0.00006797259],"domain_scores_gemma":[0.9996399,0.00008251311,0.00006472002,0.00005236681,0.0001356981,0.00002476166],"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":[0.001076501,0.0003474961,0.001672974,0.001066718,0.0002976759,0.0003215303,0.0003116498,0.0222162,0.1070334,0.01482863,0.06109942,0.7897278],"study_design_scores_gemma":[0.0003332678,0.0006602193,0.003103266,0.0001523124,0.0002357735,0.001489584,0.0002988033,0.5097955,0.1625731,0.03497801,0.2861823,0.0001978734],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008622395,0.001988782,0.9722897,0.0002641173,0.0002598012,0.0003860733,0.001879898,0.01090278,0.003406555],"genre_scores_gemma":[0.01437341,0.0003799375,0.9788679,0.00008465563,0.0000231614,0.0003007721,0.002754867,0.0003273287,0.00288803],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006132355,"threshold_uncertainty_score":0.02051479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01425325673651598,"score_gpt":0.2596864115988495,"score_spread":0.2454331548623335,"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."}}