{"id":"W1821705883","doi":"10.1109/mmsp.1998.738974","title":"Order statistics preserving near-lossless image coding","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Lossless compression; Fidelity; Computer science; Data compression; Coding (social sciences); Context (archaeology); Image compression; Image (mathematics); Theoretical computer science; Algorithm; Mathematics; Artificial intelligence; Statistics; Image processing; Telecommunications; Geography","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001070409,0.0001253332,0.0001259653,0.00004855287,0.0001916754,0.000372265,0.001317627,0.00003798406,0.0009566345],"category_scores_gemma":[0.0001528324,0.0001104068,0.00001744446,0.0003247446,0.00005660639,0.001272295,0.00108791,0.0001406041,0.0001968712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002308919,"about_ca_system_score_gemma":0.00001199363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002131167,"about_ca_topic_score_gemma":0.000003153184,"domain_scores_codex":[0.9988506,0.00004247544,0.0002043684,0.0003502792,0.0002701653,0.0002821246],"domain_scores_gemma":[0.9986882,0.0001588404,0.00007118775,0.0008424019,0.0001473648,0.00009204113],"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":[0.000002389332,0.0001475962,0.0002269457,0.0000537315,0.00001351377,0.0001003426,0.0005112269,0.0001282087,0.007081829,0.3331282,0.4970462,0.1615597],"study_design_scores_gemma":[0.0001671136,0.00003066697,0.0001416076,0.00003229902,0.000002174773,0.00001387693,0.00001058534,0.9283485,0.0186727,0.01431328,0.03799226,0.0002749832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001321366,0.00004967477,0.9824373,0.000366746,0.0001033422,0.0001139821,0.00001732538,0.000824904,0.01595461],"genre_scores_gemma":[0.0145678,0.00004423406,0.9826018,0.0003187287,0.00002278255,0.00001372634,0.000004288257,0.00001229453,0.002414313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9282203,"threshold_uncertainty_score":0.9999566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02810671206283039,"score_gpt":0.2837483604976889,"score_spread":0.2556416484348585,"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."}}