{"id":"W4398186256","doi":"10.1109/icc-robins60238.2024.10533927","title":"Smart Shopping Trolley based on IoT and AI for the Visually Impaired","year":2024,"lang":"en","type":"article","venue":"","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"PricewaterhouseCoopers (Canada)","funders":"","keywords":"Computer science; Obstacle; Raspberry pi; Visually impaired; Internet of Things; Product (mathematics); Optical character recognition; Path (computing); Work (physics); Human–computer interaction; Embedded system; Multimedia; Computer security; Artificial intelligence; Engineering; Computer network","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":[0.0004987433,0.0001088238,0.0001125079,0.0001212406,0.00008001931,0.0001984126,0.0001021179,0.00004944291,0.00006294549],"category_scores_gemma":[0.00005949846,0.00007030407,0.0000624667,0.0001559961,0.00001843981,0.00003119459,0.00001614081,0.0001357019,0.00003720273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005589351,"about_ca_system_score_gemma":0.0000324999,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002186104,"about_ca_topic_score_gemma":0.00003666686,"domain_scores_codex":[0.9992599,0.00002300873,0.0001329589,0.0001597325,0.0001798384,0.0002444968],"domain_scores_gemma":[0.9988568,0.0008789873,0.000003663325,0.0001878955,0.0000212663,0.00005141475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004012361,0.0001029377,0.006892191,0.00545875,0.001314031,0.00009198405,0.001642892,0.2432903,0.05535934,0.01020657,0.5055095,0.1697303],"study_design_scores_gemma":[0.0002518072,0.00006110674,0.0009503046,0.0001005728,0.000008205199,0.000002888851,0.00003084644,0.9347861,0.0006894188,0.0000136112,0.06301775,0.00008732542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3159189,0.005964654,0.5931097,0.01294845,0.005559415,0.005799953,0.00006382829,0.005717504,0.05491761],"genre_scores_gemma":[0.997779,0.000005920185,0.0002165526,0.0001714689,0.0002029358,0.0001636385,0.000001946779,0.00004506721,0.001413454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6914958,"threshold_uncertainty_score":0.2866917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163360428527419,"score_gpt":0.2966686092526706,"score_spread":0.2750350049673964,"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."}}