{"id":"W4385407477","doi":"10.2139/ssrn.4521008","title":"Perceptual Quality Assessment of Underwater Image Enhancement","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Underwater; Perception; Image quality; Quality (philosophy); Artificial intelligence; Computer science; Computer vision; Image (mathematics); Psychology; Geology; Physics; Oceanography","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.007069161,0.000471469,0.0006862022,0.0003932637,0.000173546,0.000329044,0.002909448,0.0002479684,0.00006926277],"category_scores_gemma":[0.00005525591,0.0004335918,0.0004131057,0.0002629572,0.0001306909,0.0004945405,0.002529434,0.004814306,0.00005489881],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003120591,"about_ca_system_score_gemma":0.005913942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001800264,"about_ca_topic_score_gemma":0.0001869856,"domain_scores_codex":[0.9934101,0.0005033487,0.00121685,0.0008165418,0.001288076,0.002765084],"domain_scores_gemma":[0.9971081,0.00009259288,0.001005675,0.001281932,0.0004062729,0.0001054367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004677213,0.001173944,0.001005272,0.0005633074,0.001821925,0.00005410909,0.002316921,0.0002181969,0.1116912,0.8022517,0.00363388,0.07522276],"study_design_scores_gemma":[0.0006708976,0.0008087381,0.00194233,0.0003054811,0.00007222414,0.00007177462,0.000750355,0.003632406,0.032641,0.9578007,0.0003420657,0.0009619549],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01689717,0.0002899404,0.9780907,0.001572558,0.0009086558,0.0004517305,0.000006710663,0.0003469251,0.001435581],"genre_scores_gemma":[0.8577068,0.005357527,0.1316255,0.0001264753,0.000322052,0.00009487851,0.00002862945,0.00007062153,0.004667551],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8464653,"threshold_uncertainty_score":0.9998116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04170734470321166,"score_gpt":0.3588900650341877,"score_spread":0.3171827203309761,"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."}}