{"id":"W2296191973","doi":"10.1364/vsia.2001.fd2","title":"Image enhancement filters for the visually impaired: a comparison of generic and customised niters.","year":2001,"lang":"en","type":"article","venue":"","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Unsharp masking; Masking (illustration); Computer vision; Image enhancement; Contrast (vision); Contrast enhancement; Computer science; Artificial intelligence; Visually impaired; Image (mathematics); Visual masking; Low vision; Optometry; Human–computer interaction; Psychology; Visual perception; Medicine; Art; Perception","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":[0.002175342,0.0003934482,0.0004035308,0.000705806,0.0001293846,0.0004688661,0.0004436744,0.0007274952,0.002728314],"category_scores_gemma":[0.004553774,0.0001183763,0.0004215651,0.0002979957,0.0002704583,0.0007187627,0.0003285608,0.0002459635,0.0005676161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002398172,"about_ca_system_score_gemma":0.0002141986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005330548,"about_ca_topic_score_gemma":0.0009138728,"domain_scores_codex":[0.9994801,0.0001519223,0.00007626213,0.00007478922,0.0001757658,0.00004128457],"domain_scores_gemma":[0.9983951,0.0008406474,0.0001488458,0.0002385137,0.000290381,0.00008642795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.008544803,0.0008085155,0.009360466,0.002225854,0.0002696271,0.0006297043,0.0004319866,0.002432119,0.2222058,0.001626857,0.001297003,0.7501672],"study_design_scores_gemma":[0.001278208,0.04355431,0.3087314,0.001064677,0.003100807,0.04309779,0.001686658,0.03621605,0.4997858,0.003227587,0.05783687,0.0004198445],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8803242,0.01294925,0.09657152,0.0002188734,0.0001288208,0.0003107939,0.0002790283,0.0008522678,0.00836524],"genre_scores_gemma":[0.8938982,0.008913439,0.09001032,0.0002369445,0.00006612875,0.0001199652,0.0004085024,0.0001548603,0.006191582],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002728314,"threshold_uncertainty_score":0.01150447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09981279187033622,"score_gpt":0.3746214649515344,"score_spread":0.2748086730811982,"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."}}