{"id":"W2135448764","doi":"10.1109/icip.1994.413281","title":"Adaptive MHDCT coding of images","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Discrete cosine transform; Hadamard transform; Coding (social sciences); ENCODE; Transform coding; Computer science; Algorithm; Walsh function; Artificial intelligence; Decoding methods; Computer vision; Pattern recognition (psychology); Theoretical computer science; Mathematics; Image (mathematics); Statistics; Gene","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":[0.00005738562,0.00005969923,0.00009188157,0.00005975867,0.00003015746,0.00001685812,0.0006471052,0.00001855668,0.0001746505],"category_scores_gemma":[0.0000257935,0.00004811881,0.00002494007,0.0001658363,0.00003378534,0.0005930731,0.0003630746,0.0000513352,0.00003359779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008783636,"about_ca_system_score_gemma":0.000002903045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006355921,"about_ca_topic_score_gemma":2.72709e-7,"domain_scores_codex":[0.9994389,0.00002015138,0.0001246012,0.0001755197,0.0001345291,0.000106255],"domain_scores_gemma":[0.9993255,0.0000735659,0.00006035645,0.0004610262,0.000047482,0.00003203995],"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":[0.000003149054,0.0001220232,0.0001186239,0.00001225284,0.00001086503,0.00001609237,0.0002810307,0.00003111537,0.04484303,0.4531654,0.1012854,0.400111],"study_design_scores_gemma":[0.0001596598,0.0001276442,0.0002671409,0.00005164112,0.000001764652,0.00001084537,0.00002600258,0.1076548,0.8673913,0.01779681,0.006317949,0.0001944215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00006977537,0.0001009663,0.9645441,0.0001489589,0.00003547701,0.00005286783,0.000002905774,0.0003119043,0.03473307],"genre_scores_gemma":[0.4275571,0.00003975107,0.5716027,0.00007515952,0.00000676493,0.000003646521,2.116653e-7,0.000002571583,0.0007120581],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8225483,"threshold_uncertainty_score":0.1962228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03910766474107353,"score_gpt":0.2671424620582969,"score_spread":0.2280347973172234,"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."}}