{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002149279,0.0006464703,0.0003006989,0.0005295216,0.0001699443,0.0003194207,0.0005457571,0.000657381,0.004786421],"category_scores_gemma":[0.0006328397,0.0002087129,0.0002501162,0.0002113232,0.0001145569,0.0003834246,0.0004938725,0.0004878292,0.002231255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002445977,"about_ca_system_score_gemma":0.0003388652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002056723,"about_ca_topic_score_gemma":0.004459469,"domain_scores_codex":[0.999863,0.00001614305,0.000007148952,0.00003695927,0.00005087074,0.00002585572],"domain_scores_gemma":[0.9998247,0.00004587634,0.00001039169,0.00001981671,0.00007506859,0.00002411779],"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":[0.001058339,0.0006824075,0.00510152,0.0004227707,0.0000881137,0.001151772,0.0002321332,0.005324137,0.1564865,0.0008831911,0.04509889,0.7834702],"study_design_scores_gemma":[0.0002488258,0.001452941,0.02918703,0.0003321187,0.0001801724,0.003911425,0.0003685032,0.6371871,0.2618862,0.005499382,0.0595884,0.0001578236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2644784,0.002052372,0.6341687,0.001478007,0.0005042038,0.0009451676,0.00342504,0.0760446,0.01690355],"genre_scores_gemma":[0.7887268,0.0006944264,0.1982845,0.0008673887,0.00008166322,0.0003207494,0.001770197,0.0002836384,0.008970636],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004786421,"threshold_uncertainty_score":0.01601219,"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."}}