{"id":"W2751149696","doi":"10.4230/lipics.cpm.2023.26","title":"Acceleration of FM-Index Queries Through Prefix-Free Parsing","year":2017,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"International Max Planck Research School for Environmental, Cellular and Molecular Microbiology; Shurl and Kay Curci Foundation; California State University, Dominguez Hills; Gordon and Betty Moore Foundation; Pennsylvania Department of Health; National Institutes of Health; National Science Foundation","keywords":"Computer science; Identification (biology); Source code; Metric (unit); Data mining; Pattern recognition (psychology); Artificial intelligence; 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":[0.001414009,0.002116119,0.001537293,0.002333435,0.0008684116,0.002639414,0.0030875,0.001535775,0.01022438],"category_scores_gemma":[0.009231922,0.0008479979,0.001362998,0.004202492,0.0007411541,0.005604698,0.002518654,0.001520897,0.009171646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001073393,"about_ca_system_score_gemma":0.001738304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003716511,"about_ca_topic_score_gemma":0.003537101,"domain_scores_codex":[0.9973813,0.000353545,0.0003382522,0.0006359266,0.001007245,0.0002836823],"domain_scores_gemma":[0.995587,0.001974811,0.0002106454,0.001350763,0.0007440376,0.000132775],"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":[0.00138804,0.0004593042,0.003916181,0.0008877006,0.0001690232,0.0003715769,0.0007165734,0.01656576,0.0960034,0.01530298,0.04976549,0.814454],"study_design_scores_gemma":[0.000378161,0.0004059068,0.003617365,0.0001028746,0.0001688783,0.001022608,0.000461669,0.726606,0.1515022,0.04278157,0.07275247,0.0002003128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06334487,0.001649929,0.7904033,0.0005313071,0.0003409379,0.000349522,0.003668266,0.1294469,0.01026496],"genre_scores_gemma":[0.1614482,0.0004603463,0.8129078,0.0003439108,0.0001693573,0.0003227954,0.01137979,0.006482953,0.00648483],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01022438,"threshold_uncertainty_score":0.03420395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02438688718006604,"score_gpt":0.2813948630964302,"score_spread":0.2570079759163642,"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."}}