{"id":"W1490366898","doi":"10.1109/icip.2003.1246669","title":"A new decoding algorithm based on range block mean and contrast scaling","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Decoding methods; Algorithm; Contrast (vision); Sequential decoding; Block (permutation group theory); Computer science; Scaling; Fractal; List decoding; Range (aeronautics); Berlekamp–Welch algorithm; Mathematics; Block code; Artificial intelligence; Concatenated error correction code; Combinatorics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004192203,0.0006972698,0.0007517156,0.001138307,0.00040981,0.0008761536,0.0007432579,0.001040705,0.002434688],"category_scores_gemma":[0.002151822,0.0002926518,0.0004972495,0.0009561668,0.0005257053,0.001508464,0.0007964631,0.001081473,0.00183991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004589949,"about_ca_system_score_gemma":0.0006332491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001325363,"about_ca_topic_score_gemma":0.001464701,"domain_scores_codex":[0.9994467,0.00005170217,0.00003988192,0.00008710014,0.0003420078,0.00003256707],"domain_scores_gemma":[0.9992316,0.000230882,0.00005551449,0.0001209396,0.0003233511,0.00003768311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001628311,0.00005061186,0.0007511641,0.000158514,0.00005329628,0.0001741264,0.0001272085,0.03476586,0.1348945,0.02954403,0.005440974,0.7938769],"study_design_scores_gemma":[0.00006144613,0.0001297515,0.0006902249,0.00003228979,0.00005285492,0.001456886,0.00003133543,0.8513494,0.1130618,0.009796088,0.02325569,0.00008226487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005006076,0.0002459454,0.9925662,0.00006448349,0.0001013588,0.00003000162,0.00003795875,0.0006336162,0.001314358],"genre_scores_gemma":[0.05092195,0.0003824898,0.9426287,0.0001041437,0.0001377943,0.0001108472,0.0002484379,0.0002276394,0.005237909],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002434688,"threshold_uncertainty_score":0.008144915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01393746621791583,"score_gpt":0.2633497287284444,"score_spread":0.2494122625105286,"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."}}