{"id":"W2047856763","doi":"10.1049/iet-ipr.2012.0489","title":"Is there a relationship between peak‐signal‐to‐noise ratio and structural similarity index measure?","year":2013,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Measure (data warehouse); Similarity (geometry); Index (typography); Signal-to-noise ratio (imaging); Noise (video); SIGNAL (programming language); Similarity measure; Structural similarity; Pattern recognition (psychology); Mathematics; Statistics; Computer science; Artificial intelligence; Speech recognition; Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004837648,0.0002125413,0.000220811,0.0001116195,0.0004879318,0.001712282,0.0005131391,0.00008934598,0.00004231132],"category_scores_gemma":[0.000134941,0.0001884407,0.00004714393,0.0004297939,0.00007751175,0.003106794,0.0002645007,0.0003187738,0.00005886705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005280817,"about_ca_system_score_gemma":0.0001753908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001333222,"about_ca_topic_score_gemma":0.000008162609,"domain_scores_codex":[0.9982004,0.0001523775,0.0003381854,0.0004928063,0.0004591293,0.0003571741],"domain_scores_gemma":[0.9987949,0.0001581421,0.0001450947,0.0003788238,0.000330596,0.0001924041],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001335283,0.00006393298,0.7074202,0.0005374148,0.00005295887,0.00001988366,0.02123673,0.00002962031,0.01051467,0.001148588,0.003543495,0.2554191],"study_design_scores_gemma":[0.0003804062,0.00004377443,0.9332429,0.0001225434,0.00002002245,0.00001133904,0.0002905967,0.03718904,0.004697929,0.02349066,0.0001055519,0.0004052289],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2134467,0.0003293124,0.778213,0.006833817,0.000041351,0.0003522834,0.000004660797,0.0001526178,0.0006262548],"genre_scores_gemma":[0.9382475,9.52272e-7,0.06024472,0.001203394,0.0001082082,0.00003015565,0.000003229019,0.00001456085,0.0001472941],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7248008,"threshold_uncertainty_score":0.999324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05674422087945397,"score_gpt":0.3303436820595361,"score_spread":0.2735994611800821,"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."}}