{"id":"W2125673533","doi":"10.1109/dcc.1991.213348","title":"Semantic data compression","year":2002,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; IBM (Canada)","funders":"","keywords":"Computer science; USable; Representation (politics); Object (grammar); Process (computing); Channel (broadcasting); Limit (mathematics); Space (punctuation); Theoretical computer science; Information retrieval; Programming language; Artificial intelligence; World Wide Web; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.00009158678,0.0000723296,0.00007779786,0.00003456532,0.0001069313,0.0001345545,0.002315608,0.00002667135,0.0004557944],"category_scores_gemma":[0.00001230571,0.00005100652,0.00001270063,0.0001441404,0.0000144889,0.001126399,0.002280699,0.00006643653,0.0006639308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004291406,"about_ca_system_score_gemma":0.000003617975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002855449,"about_ca_topic_score_gemma":0.000001888529,"domain_scores_codex":[0.9991336,0.00002419797,0.0001121416,0.0003688894,0.0002049649,0.0001561593],"domain_scores_gemma":[0.9978277,0.00003759879,0.00002953497,0.002016746,0.00001779623,0.00007059309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[7.033813e-7,0.0001384539,0.0002045775,0.000008035751,0.000006203044,0.00002868127,0.00009666126,0.00001944853,0.0006454969,0.01827324,0.5587173,0.4218612],"study_design_scores_gemma":[0.0001133331,0.00001108553,0.0003444119,0.00001257521,0.00000111807,0.00001260226,0.000002629163,0.889522,0.0001903531,0.0004494621,0.1092576,0.00008291483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003374935,0.0002996618,0.9805121,0.0009449208,0.0002987274,0.00005377997,0.000006058272,0.0002511872,0.01729608],"genre_scores_gemma":[0.536224,0.0001669536,0.4562528,0.001189309,0.0002031284,0.000003449351,0.00006269373,0.0000120698,0.005885611],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8895025,"threshold_uncertainty_score":0.8533709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0946497546882956,"score_gpt":0.2733762956005216,"score_spread":0.178726540912226,"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."}}