{"id":"W2157401329","doi":"10.1109/icip.1994.413860","title":"Improving the picture quality of cable television","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cable television; Quality (philosophy); Noise (video); Computer science; SIGNAL (programming language); Scheme (mathematics); Telecommunications; Multimedia; Electrical engineering; Engineering; Artificial intelligence; Physics; Mathematics; Image (mathematics)","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":[0.0001917682,0.0002870089,0.0001886222,0.0003958856,0.0001684758,0.0006634709,0.0002702178,0.0004165362,0.004314409],"category_scores_gemma":[0.001542311,0.0001044466,0.0001081012,0.0003693487,0.0002154597,0.0007352221,0.000422504,0.0003999864,0.0009073729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002743189,"about_ca_system_score_gemma":0.0001576811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009819929,"about_ca_topic_score_gemma":0.0007113662,"domain_scores_codex":[0.9997343,0.0000391182,0.000007078283,0.00003240558,0.0001635771,0.00002346451],"domain_scores_gemma":[0.9996547,0.00007090437,0.00003876678,0.00003272585,0.0001764344,0.00002652274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006909416,0.0000676588,0.003416119,0.0003152578,0.0000300659,0.0002765638,0.0002112958,0.01212618,0.5716143,0.005162501,0.00458372,0.4015054],"study_design_scores_gemma":[0.0001083624,0.001114053,0.02069964,0.00009392321,0.0001598534,0.001848085,0.0002145614,0.151208,0.7902042,0.004232604,0.03003395,0.00008271882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6711566,0.003796865,0.2966936,0.0008033817,0.0001597348,0.00005692599,0.0002612744,0.001811745,0.02526001],"genre_scores_gemma":[0.9208111,0.002578137,0.06541692,0.0001522448,0.0001532287,0.0000178208,0.0003340227,0.0002975687,0.01023897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004314409,"threshold_uncertainty_score":0.01443321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05790725621585365,"score_gpt":0.3133436320185656,"score_spread":0.2554363758027119,"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."}}