{"id":"W2143929606","doi":"10.1109/11.892156","title":"Adaptive unequal error protection for subband image coding","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Broadcasting","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Philips (Canada); University of British Columbia","funders":"","keywords":"Computer science; Channel (broadcasting); Codec; Algorithm; Convolutional code; Decoding methods; Forward error correction; Additive white Gaussian noise; Coding gain; Variable-length code; Coding (social sciences); Code rate; Binary erasure channel; Channel capacity; Mathematics; Telecommunications; Statistics","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":[0.0003052258,0.0003354923,0.0003379071,0.0002315813,0.0001599147,0.0003172108,0.0004905751,0.0003134879,0.0008011879],"category_scores_gemma":[0.0007883624,0.00009271605,0.000207897,0.0002360455,0.0002677729,0.0004790663,0.0004364405,0.0003890533,0.0002025726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003174928,"about_ca_system_score_gemma":0.0002561795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004792646,"about_ca_topic_score_gemma":0.0005267431,"domain_scores_codex":[0.9997181,0.00007239588,0.00001268773,0.00005688241,0.0001141218,0.00002578496],"domain_scores_gemma":[0.9996137,0.000114459,0.00007367948,0.00009470036,0.00008833958,0.00001521762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000658507,0.00009214581,0.00161702,0.0001680481,0.00007606595,0.0002500909,0.0002060325,0.1644689,0.4622473,0.03231633,0.001086408,0.3368132],"study_design_scores_gemma":[0.0000252183,0.0002322066,0.0006501622,0.00001335338,0.00002556335,0.0002244799,0.00001369665,0.8192375,0.1741126,0.002620746,0.002821937,0.00002256758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1241975,0.0004373782,0.8722757,0.0000938294,0.0000364121,0.00004427464,0.00003640161,0.0003885789,0.002490007],"genre_scores_gemma":[0.8383938,0.0002182883,0.1592378,0.00004699604,0.0000171903,0.00003669565,0.00004315837,0.00001826837,0.001987861],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008011879,"threshold_uncertainty_score":0.002680182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05623890181415832,"score_gpt":0.2981866988243733,"score_spread":0.241947797010215,"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."}}