{"id":"W4391923846","doi":"10.1109/whispers61460.2023.10430716","title":"Indoor Sign Recognition System for Visually Impaired People","year":2023,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Visually impaired; Computer science; Sign (mathematics); Computer vision; Artificial intelligence; Human–computer interaction; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002324666,0.0004659785,0.0006349494,0.0006569085,0.0002172051,0.0004774693,0.0006191605,0.0005727835,0.008293536],"category_scores_gemma":[0.0006555745,0.0001470581,0.0003951919,0.0002651825,0.0001328761,0.0004900346,0.0008273267,0.0005000074,0.005485683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000340391,"about_ca_system_score_gemma":0.0005380058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003567738,"about_ca_topic_score_gemma":0.005885745,"domain_scores_codex":[0.9998375,0.00001406198,0.00001468305,0.00004319598,0.00005287477,0.00003758476],"domain_scores_gemma":[0.9998057,0.00001312915,0.00001715026,0.00003159992,0.0001098598,0.00002253895],"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":[0.0008702766,0.000243984,0.009036604,0.0003490151,0.00008971955,0.001288387,0.0001134782,0.00380693,0.1460501,0.0008737116,0.03275671,0.8045211],"study_design_scores_gemma":[0.0002526378,0.001768327,0.09380902,0.0003811172,0.0004287975,0.01180037,0.0004628415,0.453742,0.3590999,0.003222868,0.07475093,0.0002812125],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5274368,0.001697934,0.3836995,0.0008069542,0.001051815,0.0006783122,0.006298495,0.04615973,0.03217043],"genre_scores_gemma":[0.8321435,0.0007069034,0.1343851,0.0005398827,0.000121888,0.0003608198,0.00592108,0.0002988316,0.02552186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008293536,"threshold_uncertainty_score":0.02774465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03567085977460783,"score_gpt":0.3143699550588451,"score_spread":0.2786990952842373,"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."}}