{"id":"W2503041365","doi":"10.2140/involve.2016.9.657","title":"Avoiding approximate repetitions with respect to the longest common subsequence distance","year":2016,"lang":"en","type":"article","venue":"Involve a Journal of Mathematics","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Longest common subsequence problem; Hamming distance; Edit distance; Combinatorics; Mathematics; Lemma (botany); Similarity (geometry); Repetition (rhetorical device); Longest increasing subsequence; Entropy (arrow of time); Subsequence; Discrete mathematics; Algorithm; Computer science; Artificial intelligence; Physics; Mathematical analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.001194583,0.0001222627,0.0002076233,0.00007592264,0.0002682762,0.0001714413,0.001299833,0.00002592325,0.00000714513],"category_scores_gemma":[0.0002745361,0.00005198415,0.00005744317,0.0002857526,0.00007144581,0.0006287073,0.0002202354,0.0002136375,0.00002168132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006065903,"about_ca_system_score_gemma":0.0000689683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003731357,"about_ca_topic_score_gemma":0.00002726658,"domain_scores_codex":[0.9986642,0.0000960052,0.0004044386,0.0001491451,0.0004850111,0.0002011779],"domain_scores_gemma":[0.9978853,0.0006447228,0.0004363565,0.0006870297,0.0002270721,0.0001194446],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001637639,0.001101031,0.001032647,0.0002773792,0.0002281133,0.001368363,0.02524205,0.0009626646,0.01855434,0.8659822,0.02274967,0.06233782],"study_design_scores_gemma":[0.005461359,0.005304022,0.002811689,0.0338027,0.0003060962,0.01796995,0.007933632,0.08261039,0.06849507,0.6371275,0.1351648,0.003012807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02310113,0.000295268,0.9710782,0.005023526,0.0001251268,0.0001144747,0.000009403485,0.00002540306,0.0002275352],"genre_scores_gemma":[0.6480156,0.0001345747,0.3512567,0.0002495155,0.0001620782,0.000008436429,3.705648e-7,0.00001567115,0.0001571249],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6249144,"threshold_uncertainty_score":0.2415435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03174359769984818,"score_gpt":0.2618504691778532,"score_spread":0.230106871478005,"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."}}