{"id":"W2118573057","doi":"10.1109/icme.2008.4607544","title":"Likelihood-based non-uniform allocation of Gaussian kernels in scalar dimension for HMM compression","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Representation (politics); Hidden Markov model; Gaussian; Computer science; Dimension (graph theory); Kullback–Leibler divergence; Pattern recognition (psychology); Divergence (linguistics); Scalar (mathematics); Gaussian process; Artificial intelligence; Algorithm; 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":[0.0002465808,0.0001685991,0.0002746081,0.0002602809,0.0001059408,0.0000155357,0.0007964174,0.0001023529,0.00001146797],"category_scores_gemma":[0.00004590535,0.0001382895,0.00006680076,0.0003875704,0.00006303738,0.0007423461,0.0002818482,0.0001095614,0.000006720224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005418957,"about_ca_system_score_gemma":0.00009273439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006742938,"about_ca_topic_score_gemma":0.00001253261,"domain_scores_codex":[0.9984726,0.00004028185,0.0004577854,0.0004298535,0.0003163603,0.0002831077],"domain_scores_gemma":[0.9985475,0.0001564695,0.00020036,0.0008538219,0.0001471469,0.00009468926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00035623,0.002237019,0.01207865,0.0004014408,0.00002272415,0.00003290462,0.001088881,0.01045361,0.752099,0.034953,0.02112374,0.1651528],"study_design_scores_gemma":[0.0007318924,0.0001537,0.005417204,0.0001849525,0.000001627608,0.000003849749,0.000007937455,0.4358227,0.5533807,0.003338279,0.0007844912,0.0001726703],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02533229,0.00002886603,0.9729401,0.0003239888,0.00008684839,0.0006005442,0.000006688439,0.0002273653,0.0004532767],"genre_scores_gemma":[0.5651327,0.00001474841,0.4345891,0.0001310751,0.000009238343,0.00005094087,0.000023484,0.000008801496,0.00003990424],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5398004,"threshold_uncertainty_score":0.5639282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01965419480334763,"score_gpt":0.2784846525677566,"score_spread":0.258830457764409,"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."}}