{"id":"W3200072832","doi":"","title":"A Note on Lempel-Ziv Parser Tails and Substring Lengths","year":2018,"lang":"en","type":"article","venue":"IEICE Proceedings Series","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Substring; Parsing; String (physics); Symbol (formal); Algorithm; Computer science; Bernoulli's principle; String searching algorithm; Combinatorics; Mathematics; Set (abstract data type); Artificial intelligence; Pattern matching; Physics","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":[0.01168258,0.001871271,0.001749808,0.005476453,0.001752529,0.004274897,0.00339065,0.003071483,0.005333579],"category_scores_gemma":[0.09885985,0.001790649,0.001544954,0.008001935,0.0064058,0.01393676,0.003916838,0.01035488,0.002627843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003625307,"about_ca_system_score_gemma":0.002434013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002618777,"about_ca_topic_score_gemma":0.00155732,"domain_scores_codex":[0.9907497,0.002738615,0.0007397612,0.001516993,0.003627917,0.0006270448],"domain_scores_gemma":[0.9120358,0.07184183,0.003836525,0.007845148,0.003696777,0.0007438577],"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":[0.0002711353,0.00007149976,0.004137337,0.0002857917,0.00004889345,0.0005200017,0.0006453643,0.05258773,0.004903251,0.7880908,0.01134727,0.1370908],"study_design_scores_gemma":[0.00002221035,0.0001524538,0.002488137,0.0002701565,0.00003689582,0.0009212113,0.0001042016,0.1668465,0.01354039,0.7922221,0.0232302,0.0001657367],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0121249,0.007179981,0.9630224,0.00283091,0.0004011368,0.00006881812,0.0005789082,0.001338679,0.01245425],"genre_scores_gemma":[0.3024696,0.01638974,0.6498919,0.003545305,0.004720606,0.0008389072,0.002487061,0.004283361,0.01537357],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01168258,"threshold_uncertainty_score":0.06178415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243132639675895,"score_gpt":0.2450694042685167,"score_spread":0.2326380778717577,"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."}}