{"id":"W2005157037","doi":"10.1109/isit.2011.6034036","title":"Scalar quantization with Rényi entropy constraint","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Companding; Quantization (signal processing); Mathematics; Entropy (arrow of time); Vector quantization; Rate–distortion theory; Constructive; Applied mathematics; Rate distortion; Statistical physics; Algorithm; Computer science; Physics; Quantum mechanics; Estimator; Data compression; Statistics; Coding (social sciences)","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":[0.0001129995,0.0001269948,0.0001199366,0.0000852053,0.00007533612,0.00005673917,0.000741331,0.00004324112,0.0002604116],"category_scores_gemma":[0.00002746856,0.00008980127,0.00003014175,0.0002464781,0.00009669714,0.000797072,0.0002539571,0.00009019722,0.0001110621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001969936,"about_ca_system_score_gemma":0.00003987351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002917756,"about_ca_topic_score_gemma":0.00001058044,"domain_scores_codex":[0.9989792,0.00004135359,0.000174668,0.0003677593,0.0002254002,0.0002116021],"domain_scores_gemma":[0.998858,0.00003279977,0.00008554578,0.0008285875,0.0001016289,0.00009338023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000120392,0.00009354916,0.0009453393,0.00000580719,0.00001138559,0.00001019988,0.0003383079,0.000006041696,0.007209844,0.9670933,0.002149561,0.02212464],"study_design_scores_gemma":[0.001921921,0.0009603964,0.009780696,0.0002899663,0.00003466097,0.0003530172,0.0002168531,0.02291936,0.6741113,0.1770823,0.110328,0.002001498],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001637111,0.0000157014,0.9880428,0.0001116931,0.00006808323,0.0001665929,0.000002788387,0.0009281791,0.009027088],"genre_scores_gemma":[0.2455408,0.00001137619,0.7539599,0.0002403811,0.00001150879,0.00001316014,0.000006501932,0.000007392336,0.0002089971],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7900109,"threshold_uncertainty_score":0.3661989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04305476764432897,"score_gpt":0.2598530599183586,"score_spread":0.2167982922740296,"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."}}