{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002782306,0.00007004113,0.0001995313,0.0001457773,0.00005802643,0.00002098789,0.00002941844,0.00003393951,0.00006196612],"category_scores_gemma":[0.0001159902,0.0000534359,0.0001379987,0.000423094,0.000008392254,0.00003723455,0.00001009338,0.00004019869,0.0005006369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003180014,"about_ca_system_score_gemma":0.00003275756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006503995,"about_ca_topic_score_gemma":0.000008420415,"domain_scores_codex":[0.9993747,0.00001882763,0.0001604229,0.0001557611,0.0001267094,0.0001635649],"domain_scores_gemma":[0.9995605,0.00008962667,0.00003710292,0.0001078503,0.0001297542,0.00007515349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001737751,0.0006101862,0.1229401,0.006937684,0.001615074,0.0002705777,0.002736905,0.00003599624,0.2203753,0.0005266358,0.2699357,0.3722781],"study_design_scores_gemma":[0.02193813,0.004909527,0.243407,0.003762267,0.005910167,0.0006725324,0.05016393,0.3813145,0.2689653,0.001398543,0.01539998,0.0021581],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859129,0.00001813269,0.00488543,0.002141125,0.0001033492,0.0003866838,0.00001245329,0.0007350151,0.005804964],"genre_scores_gemma":[0.9909752,0.000007249912,0.000878701,0.0001560583,0.0001526187,0.0000647291,0.0001980675,0.0000166536,0.007550727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3812785,"threshold_uncertainty_score":0.6434842,"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."}}