{"id":"W4297684246","doi":"10.1109/iri54793.2022.00067","title":"Identifying universal safety signs using computer vision for an assistive feedback mobile application","year":2022,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Hazard; Vital signs; Sign (mathematics); Traffic sign; Signs and symptoms; Mobile device; Warning signs; Human–computer interaction; Bounding overwatch; Artificial intelligence; Computer vision; Engineering; Transport engineering; Medicine","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":[0.0001036981,0.00009765317,0.00009824381,0.0000632954,0.0002942685,0.00003249842,0.0001132845,0.00002924419,0.00003515129],"category_scores_gemma":[8.612209e-7,0.0001046028,0.00004337492,0.0001135398,0.00001076069,0.0002103592,0.00006787415,0.0001084992,0.000002119362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003185244,"about_ca_system_score_gemma":0.00001139343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002956359,"about_ca_topic_score_gemma":0.000005654316,"domain_scores_codex":[0.9994001,0.00001425302,0.0001275237,0.0001691689,0.0001046033,0.0001843468],"domain_scores_gemma":[0.9997403,0.00002375206,0.00002697069,0.000134153,0.00003712025,0.00003769333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003466287,0.00001303326,0.0001118719,0.00002836045,0.00002175917,0.000001260148,0.0003682362,0.8567971,0.1117381,0.0006280601,0.0005994289,0.02965816],"study_design_scores_gemma":[0.0003141394,0.0001850789,0.0009445513,0.000009608518,0.00001849366,0.000008104293,0.001238507,0.9764641,0.004410275,0.0001579233,0.01603097,0.0002182622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.110305,0.00001264311,0.8875673,0.000002912417,0.001000197,0.0003515542,0.00001907205,0.0001904858,0.0005508204],"genre_scores_gemma":[0.9674777,0.000001717023,0.03200736,0.00002019236,0.0002914222,0.00005005991,0.00005131338,0.00002853014,0.00007175233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8571726,"threshold_uncertainty_score":0.4265577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01340232246700906,"score_gpt":0.2630496721480313,"score_spread":0.2496473496810223,"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."}}