{"id":"W2921895451","doi":"10.23919/isita.2018.8664360","title":"Compression by Substring Enumeration with a Finite Alphabet Using Sorting","year":2018,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Substring; Lexicographical order; Upper and lower bounds; Enumeration; Sorting; Algorithm; Code word; Encoding (memory); Computer science; Compression (physics); Alphabet; Data compression; Combinatorics; Encoder; Pooling; Mathematics; Order (exchange); Data structure; Decoding methods; Artificial intelligence; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001432412,0.0001145931,0.0001050497,0.00005061382,0.0003558059,0.0002400139,0.0003718014,0.0000392064,0.00003613432],"category_scores_gemma":[0.00001244756,0.0000811655,0.00001676408,0.0002337323,0.00003784194,0.001051371,0.0002671741,0.00007975677,0.00002273265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001978825,"about_ca_system_score_gemma":0.00002973299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001074487,"about_ca_topic_score_gemma":0.000008476987,"domain_scores_codex":[0.9989787,0.00003440513,0.0001795733,0.0003295068,0.0002492532,0.0002285665],"domain_scores_gemma":[0.9992958,0.00005163493,0.0001110401,0.0003771995,0.00009041592,0.00007386697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001136518,0.0004184272,0.0217131,0.00005689655,0.00008228294,0.00004769785,0.0031119,0.004155892,0.5272605,0.01311161,0.01935193,0.4105761],"study_design_scores_gemma":[0.0002999328,0.00009409545,0.0002417654,0.00007510178,0.000003323624,0.000009673292,0.00002070127,0.9477779,0.04808444,0.0001633809,0.003066199,0.0001635253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0783252,0.00004409298,0.9202913,0.0000632847,0.0001317857,0.00007758789,0.000002697559,0.0001469796,0.0009170612],"genre_scores_gemma":[0.7569029,0.000002185061,0.2427079,0.0001421096,0.0001178588,0.000002328422,0.00001454602,0.000007538737,0.0001026732],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.943622,"threshold_uncertainty_score":0.3309833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01883167309484636,"score_gpt":0.2555191560520908,"score_spread":0.2366874829572444,"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."}}