{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000841841,0.0004450366,0.0003468771,0.0008217852,0.0001667817,0.0007390404,0.0002429988,0.0004122329,0.003671149],"category_scores_gemma":[0.003373772,0.0001397572,0.000213711,0.0005051816,0.0003027309,0.0005991034,0.0005169484,0.0003287604,0.0003607085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001599609,"about_ca_system_score_gemma":0.0001409954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007965697,"about_ca_topic_score_gemma":0.0005905458,"domain_scores_codex":[0.9996451,0.00008035736,0.0000208722,0.00004934247,0.0001684201,0.00003588703],"domain_scores_gemma":[0.9985678,0.0005300248,0.0001232119,0.0001004722,0.0006138394,0.00006451555],"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":[0.00287706,0.0001825917,0.007223978,0.0007978546,0.0001397677,0.0004424193,0.0002678708,0.0281632,0.5305297,0.002529225,0.001431989,0.4254144],"study_design_scores_gemma":[0.0001211147,0.002035939,0.06411304,0.0001605105,0.0004755094,0.002146389,0.0004331814,0.4539774,0.4674997,0.00336017,0.00557741,0.0000997324],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5248816,0.002083891,0.4609755,0.0002082386,0.0001371996,0.0001616083,0.0002991719,0.0006150755,0.01063776],"genre_scores_gemma":[0.9234238,0.001120373,0.07057043,0.00005458642,0.00005292146,0.00002268145,0.0002436526,0.0001433211,0.004368103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003671149,"threshold_uncertainty_score":0.01228118,"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."}}