{"id":"W2231051939","doi":"10.17485/ijst/2015/v8i24/80242","title":"A New Variable-Length Integer Code for Integer Representation and Its Application to Text Compression","year":2015,"lang":"en","type":"article","venue":"Indian Journal of Science and Technology","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Golomb coding; Lossless compression; Lossy compression; Computer science; Data compression; Systematic code; Code (set theory); Compression (physics); Algorithm; Prefix code; Universal code; Integer (computer science); Data compression ratio; Theoretical computer science; Code rate; Image compression; Linear code; Programming language; Decoding methods; Physics; Block code; Artificial intelligence","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":[0.0008663082,0.00008355831,0.0001576532,0.0006926351,0.0001694844,0.0001312845,0.0008582261,0.00008137849,0.000001245614],"category_scores_gemma":[0.0004790326,0.00006298006,0.00001142638,0.001232563,0.0001730667,0.001155017,0.0004059221,0.0001585443,0.000003561912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004474416,"about_ca_system_score_gemma":0.0003775624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000945075,"about_ca_topic_score_gemma":0.000002719698,"domain_scores_codex":[0.9989259,0.00001416833,0.0002555067,0.0002945754,0.0003105513,0.0001992465],"domain_scores_gemma":[0.9984946,0.00005345379,0.0002119134,0.0002442754,0.0007083032,0.000287468],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005287058,0.00005043859,0.0005974095,0.00001272571,0.000009900243,0.00002048421,0.002095647,0.0000901421,0.04573436,0.07922705,0.006035321,0.8660737],"study_design_scores_gemma":[0.005455409,0.004110538,0.001606398,0.0007900643,0.0000467479,0.005164426,0.004731482,0.3150032,0.154611,0.3308393,0.1766548,0.0009866514],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04384249,0.0003140232,0.9492543,0.005985627,0.0002824691,0.0002233948,0.000003879982,0.00003042161,0.00006345021],"genre_scores_gemma":[0.8316349,0.00002845206,0.1680059,0.0002006184,0.00007419182,0.000009486016,6.350183e-7,0.000004315254,0.00004149824],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.865087,"threshold_uncertainty_score":0.2568252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02299782982729939,"score_gpt":0.3002173426676524,"score_spread":0.277219512840353,"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."}}