{"id":"W2170183661","doi":"10.1109/isimp.2001.925359","title":"Noncausal predictive lattice model for image compression","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":"University of Windsor","funders":"","keywords":"Lossy compression; Lossless compression; Image compression; Computer science; Data compression; Redundancy (engineering); Algorithm; Artificial intelligence; Codec; Predictive coding; Image plane; Image (mathematics); Computer vision; Mathematics; Image processing; 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":[0.0001006821,0.0001450039,0.0001499686,0.00007201445,0.0001460464,0.00008585247,0.001029289,0.00006237531,0.00005221794],"category_scores_gemma":[0.00005665405,0.0001185101,0.00005250897,0.0001369402,0.00004505611,0.001513441,0.0004140268,0.000113956,0.00004527433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002824546,"about_ca_system_score_gemma":0.000009785401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002043163,"about_ca_topic_score_gemma":5.754222e-7,"domain_scores_codex":[0.9988261,0.00002696516,0.0002018459,0.000447496,0.0002271642,0.000270397],"domain_scores_gemma":[0.9987696,0.0001382092,0.00008085022,0.0007760023,0.0001314781,0.0001038999],"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":[0.00005078115,0.0005759882,0.00003711698,0.00006202739,0.00003001299,0.00001771091,0.001260247,0.004438355,0.03201092,0.2114788,0.6379212,0.1121169],"study_design_scores_gemma":[0.0002727361,0.00007101673,0.00001520935,0.00002218563,0.000003285653,0.000005130805,0.000004387843,0.9565091,0.01814649,0.02043185,0.004367281,0.0001513535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005076929,0.00005165663,0.9864443,0.0006393099,0.00008858766,0.0003827859,0.000032823,0.00104155,0.01126823],"genre_scores_gemma":[0.04594444,0.00001583908,0.9502032,0.000523428,0.00003905046,0.0001169329,0.000007008717,0.00001405681,0.003135986],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9520707,"threshold_uncertainty_score":0.4832702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0426688238464089,"score_gpt":0.3016464091005967,"score_spread":0.2589775852541878,"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."}}