{"id":"W6910360012","doi":"10.4230/lipics.wabi.2024.10","title":"b-move: Faster Bidirectional Character Extensions in a Run-Length Compressed Index","year":2024,"lang":"en","type":"article","venue":"Ghent University Academic Bibliography (Ghent University)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Institutes of Health; Vlaamse regering; Natural Sciences and Engineering Research Council of Canada; Fonds Wetenschappelijk Onderzoek","keywords":"RefSeq; Search engine indexing; Scalability; Lossless compression; Pattern matching; Matching (statistics); Character (mathematics); Data compression; Overhead (engineering)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.000287468,0.0003704239,0.000357868,0.03002319,0.0004008774,0.0001878854,0.001924029,0.0004253274,0.0001137979],"category_scores_gemma":[0.000005868375,0.0004115512,0.0003602362,0.03069123,0.0002004747,0.00337722,0.001430394,0.001385594,0.00008120365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000170203,"about_ca_system_score_gemma":0.000133613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002773059,"about_ca_topic_score_gemma":0.00001395849,"domain_scores_codex":[0.9970645,0.0002369357,0.0002772615,0.001110093,0.0006659278,0.0006452333],"domain_scores_gemma":[0.9985066,0.000253298,0.0001269728,0.0006169014,0.0001298194,0.0003664309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007422593,0.001442246,0.4335909,0.0004389619,0.001420225,0.007477392,0.00354321,0.001236489,0.005288955,0.2675702,0.2123872,0.06486194],"study_design_scores_gemma":[0.001502781,0.00007180739,0.2268343,0.0004679626,0.00008376339,0.00002987671,0.0004328146,0.029559,0.0000887827,0.001149968,0.7390319,0.0007469741],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1785238,0.001663935,0.8002672,0.003663586,0.002803758,0.0009303421,0.0004394231,0.001795467,0.009912441],"genre_scores_gemma":[0.9850178,0.008622096,0.00287645,0.0003416702,0.0002437829,7.520098e-7,0.000108901,0.00003089278,0.002757661],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.806494,"threshold_uncertainty_score":0.9998336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01564244966800347,"score_gpt":0.2213653353767176,"score_spread":0.2057228857087141,"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."}}