{"id":"W2065774877","doi":"10.1109/ccece.2014.6901106","title":"Weighted Ratio-Based Adaptive Lossless image coding","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lossless compression; Computer science; Pixel; Data compression; ENCODE; Sign (mathematics); Codec; Algorithm; Image compression; Compression ratio; Adaptive coding; Artificial intelligence; Encoding (memory); Image (mathematics); Coding (social sciences); Decoding methods; Pattern recognition (psychology); Mathematics; Image processing; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002087502,0.000131948,0.0001437571,0.00009745336,0.0001220968,0.0001265336,0.000985443,0.00004258216,0.00007022466],"category_scores_gemma":[0.00004876066,0.0001074038,0.00003735679,0.0002479591,0.0000539088,0.0009022991,0.0003177977,0.0001110144,0.00009682675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002965955,"about_ca_system_score_gemma":0.00003188926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000993759,"about_ca_topic_score_gemma":0.000002558854,"domain_scores_codex":[0.9988918,0.00009507247,0.0001817284,0.0003853559,0.0002308569,0.0002151425],"domain_scores_gemma":[0.9986843,0.0002062972,0.00008557866,0.000823182,0.0001147032,0.00008597604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001064059,0.00006031396,0.00003446904,0.000008116174,0.000004762103,0.000007814031,0.00004445811,0.00003829697,0.03523258,0.8861115,0.01123197,0.06721508],"study_design_scores_gemma":[0.0001999568,0.00005493108,0.00006772328,0.00002280359,0.000001228539,0.000001538955,0.000003598487,0.6217589,0.3557315,0.01573029,0.006266913,0.0001606139],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001524988,0.000007104348,0.9870017,0.0004654383,0.00009328417,0.0001342059,0.000003102173,0.001079818,0.01106285],"genre_scores_gemma":[0.3097444,0.000001272309,0.6893862,0.000636847,0.00002734076,0.00001839995,0.000004988787,0.000007218707,0.0001733001],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8703812,"threshold_uncertainty_score":0.43798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01533735998939601,"score_gpt":0.264960384097794,"score_spread":0.249623024108398,"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."}}