{"id":"W4401034109","doi":"10.1007/978-3-031-66159-4_10","title":"How to Find Long Maximal Exact Matches and Ignore Short Ones","year":2024,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Human Genome Research Institute; Università degli Studi di Milano-Bicocca","keywords":"Computer science; Algorithm; Theoretical computer science","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":[0.002409737,0.001372994,0.00297881,0.004410767,0.002112329,0.003767445,0.002944344,0.002599509,0.01607406],"category_scores_gemma":[0.01678997,0.001267124,0.001770146,0.004360419,0.001193264,0.009182259,0.003527147,0.002368066,0.009745887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007550055,"about_ca_system_score_gemma":0.003758796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003657203,"about_ca_topic_score_gemma":0.007776981,"domain_scores_codex":[0.996167,0.0003255846,0.000370374,0.00103657,0.001586796,0.0005136958],"domain_scores_gemma":[0.9914675,0.003061833,0.0005018006,0.002114703,0.002303894,0.0005501867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001430338,0.0004286657,0.008025685,0.0007797939,0.000264253,0.0004451606,0.0002795887,0.01125152,0.02601821,0.01266803,0.04226068,0.896148],"study_design_scores_gemma":[0.0005321234,0.000741058,0.006550313,0.00044791,0.0008182646,0.003893833,0.002363493,0.4951427,0.1340023,0.2569482,0.09824611,0.0003136356],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1294244,0.003068832,0.8345103,0.003582416,0.001375073,0.0008911396,0.003534364,0.01169931,0.01191412],"genre_scores_gemma":[0.1343199,0.000816752,0.8437325,0.0007385252,0.0003768618,0.0002069676,0.004615972,0.002134835,0.0130577],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01607406,"threshold_uncertainty_score":0.05377311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01619052697209217,"score_gpt":0.2570697544311087,"score_spread":0.2408792274590165,"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."}}