{"id":"W4312660955","doi":"10.1109/tcsvt.2022.3229839","title":"Image Quality Score Distribution Prediction via Alpha Stable Model","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Alpha (finance); Artificial intelligence; Computer science; Image quality; Pattern recognition (psychology); Distribution (mathematics); Statistics; Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009591976,0.0002035669,0.0003362709,0.0002873584,0.001122761,0.0001438701,0.0004820682,0.0001316426,0.000006873051],"category_scores_gemma":[0.00001112,0.0002154849,0.0001110235,0.0005810475,0.00009388533,0.0005479169,0.00001576575,0.0003872082,0.000004124493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003083184,"about_ca_system_score_gemma":0.000106713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001750223,"about_ca_topic_score_gemma":0.00001667931,"domain_scores_codex":[0.997854,0.0001621665,0.0005550623,0.0006416145,0.0003733894,0.0004137547],"domain_scores_gemma":[0.9987787,0.0001101218,0.0001960312,0.0006688917,0.00016658,0.00007970201],"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":[0.0002207298,0.003394611,0.0002949371,0.00171451,0.0006998336,0.00005324934,0.00258362,0.1403664,0.1453593,0.3275645,0.007506301,0.3702419],"study_design_scores_gemma":[0.001204719,0.0007169733,0.0000514223,0.00003303225,0.00004606422,0.0001478294,0.0005237314,0.9741149,0.00849479,0.008679599,0.005610697,0.0003762578],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01294808,0.0002147875,0.9824846,0.0009488045,0.0009306843,0.0008919339,0.0009565749,0.0005745214,0.00005005774],"genre_scores_gemma":[0.997297,0.0000264875,0.001024939,0.0001196835,0.00002414672,0.00122202,0.00003132546,0.00001724276,0.000237129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.984349,"threshold_uncertainty_score":0.8787221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04396128655844353,"score_gpt":0.2947328708105174,"score_spread":0.2507715842520739,"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."}}