{"id":"W2169465650","doi":"10.1007/11880561_31","title":"A New Algorithm for Fast All-Against-All Substring Matching","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Substring; Algorithm; Matching (statistics); Computer science; Graph; String searching algorithm; Mathematics; Theoretical computer science; Pattern matching; Data structure; Artificial intelligence; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007254051,0.000751673,0.0006795462,0.0007580119,0.0003452268,0.0011837,0.004643944,0.0004043124,0.000009203321],"category_scores_gemma":[0.00002383166,0.0006855027,0.0002367066,0.0004164724,0.0002033045,0.001170048,0.002261098,0.0008282192,0.00002761473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003372586,"about_ca_system_score_gemma":0.0006171967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001749397,"about_ca_topic_score_gemma":0.00005849408,"domain_scores_codex":[0.9949394,0.00002355416,0.0007108658,0.002106129,0.001137698,0.001082345],"domain_scores_gemma":[0.9969411,0.0004803666,0.0004049107,0.001644344,0.0001987516,0.0003304967],"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":[0.000002369225,0.0000167751,0.000002343122,0.00002080204,0.00001282195,0.0000563547,0.0002663616,0.02386297,0.0001055635,0.002819116,0.0005178325,0.9723167],"study_design_scores_gemma":[0.0005166701,0.0001141331,0.00002512254,0.0004316219,0.0000124629,0.00005271232,1.548807e-7,0.8826367,0.0007118782,0.09403626,0.02057701,0.0008852812],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001286207,0.0005702547,0.9942523,0.0004278544,0.002412896,0.0006429335,0.00003747415,0.0002902092,0.001353183],"genre_scores_gemma":[0.0008865935,0.00003791368,0.9951976,0.001509115,0.001353523,0.00001373918,0.0000675019,0.00005941849,0.0008745913],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9714314,"threshold_uncertainty_score":0.9998532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01842756406257322,"score_gpt":0.2523865924585631,"score_spread":0.2339590283959899,"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."}}