{"id":"W2154205287","doi":"10.1111/j.1475-1313.2008.00633.x","title":"Custom‐devised and generic digital enhancement of images for people with maculopathy","year":2009,"lang":"en","type":"article","venue":"Ophthalmic and Physiological Optics","topic":"Ophthalmology and Visual Impairment Studies","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University; University of Waterloo","funders":"Macular Society","keywords":"Contrast (vision); Maculopathy; Artificial intelligence; Macular degeneration; Computer vision; Filter (signal processing); Sharpening; Age-related maculopathy; Visibility; Computer science; Mathematics; Ophthalmology; Optics; Medicine; Physics; Retinopathy","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":[0.00004613364,0.0001509949,0.0004092717,0.00002270484,0.00007849015,0.000009952687,0.0000323469,0.00006732535,0.00001537676],"category_scores_gemma":[0.00003442242,0.00009070575,0.00004412957,0.00006630986,0.0001816934,0.00005227439,0.00003390784,0.00007011413,0.000001261357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006121113,"about_ca_system_score_gemma":0.00001268045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.305921e-7,"about_ca_topic_score_gemma":9.986282e-9,"domain_scores_codex":[0.9993523,0.00001056602,0.0001604944,0.0002184825,0.00007224888,0.000185905],"domain_scores_gemma":[0.9996292,0.00007156732,0.00006820447,0.00009011627,0.00006290895,0.00007801972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01288932,0.01046984,0.3015721,0.002319235,0.001094243,0.0002417165,0.001724032,0.00003819153,0.6244427,0.004430915,0.002270252,0.03850745],"study_design_scores_gemma":[0.00281908,0.03444768,0.9527249,0.0001245169,0.000195332,0.0003662157,0.0003457655,0.000363658,0.006621286,0.001643523,0.00006655068,0.0002815099],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.997565,0.0004763271,0.0002098796,0.0002503751,0.00001870165,0.0003537335,0.00001919581,0.00001250426,0.001094266],"genre_scores_gemma":[0.9965968,0.0003175668,0.002544076,0.000139396,0.00004687421,0.00001758821,0.00002887562,0.00000555416,0.0003032494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6511527,"threshold_uncertainty_score":0.3698873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03467791082905892,"score_gpt":0.3180888825534988,"score_spread":0.2834109717244399,"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."}}