{"id":"W4390450428","doi":"10.18280/ts.400627","title":"Innovative Approaches in Image Quality Assessment: A Deep Learning-Enabled Multi-Level and Multi-Scale Perspective","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Scale (ratio); Computer science; Artificial intelligence; Quality (philosophy); Deep learning; Image quality; Quality assessment; Machine learning; Image (mathematics); Data science; Reliability engineering; Engineering; Cartography; Evaluation methods; Geography; Epistemology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001478203,0.0008361298,0.0007106608,0.001357646,0.0002660154,0.002051481,0.001731204,0.001282236,0.001514023],"category_scores_gemma":[0.002904486,0.0004297114,0.000837283,0.001008164,0.001098924,0.002817252,0.002426115,0.001980491,0.0004648702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00097957,"about_ca_system_score_gemma":0.000666561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002124975,"about_ca_topic_score_gemma":0.00215955,"domain_scores_codex":[0.9992606,0.0001680131,0.00003690285,0.000176096,0.0002798265,0.00007858754],"domain_scores_gemma":[0.9990039,0.0003087395,0.0001479571,0.0001669271,0.0002745394,0.0000980012],"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":[0.0001548681,0.0001908362,0.003797777,0.0004051928,0.000253862,0.0001905052,0.0003610087,0.2783007,0.02844095,0.06930542,0.003835416,0.6147635],"study_design_scores_gemma":[0.000006304091,0.00007042041,0.0007970046,0.00004681603,0.00003583373,0.00008228364,0.00003849531,0.9516722,0.004852129,0.03933267,0.003044683,0.0000211393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005486274,0.001152059,0.991311,0.0005261619,0.00003934958,0.00002300359,0.00003197109,0.0002706478,0.001159621],"genre_scores_gemma":[0.4637679,0.002684042,0.5288245,0.0005958814,0.0002633336,0.00009151384,0.0001526217,0.0001787734,0.003441367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002124975,"threshold_uncertainty_score":0.007817626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09345719552063066,"score_gpt":0.3239238710105872,"score_spread":0.2304666754899565,"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."}}