{"id":"W7141187915","doi":"10.1109/icoias69065.2025.00007","title":"Road Sign Classification with Denoising Pipeline Approach","year":2025,"lang":"","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Pipeline (software); Noise (video); Noise reduction; Pattern recognition (psychology); Sign (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":[0.0006899419,0.0007972167,0.0009877585,0.001084593,0.0002564826,0.0008924978,0.001218197,0.001069591,0.002776884],"category_scores_gemma":[0.001038454,0.0003126653,0.0009088746,0.0006048659,0.0003536348,0.001274236,0.0008314081,0.001138968,0.002925273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005573927,"about_ca_system_score_gemma":0.001026049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005058525,"about_ca_topic_score_gemma":0.006639166,"domain_scores_codex":[0.9995486,0.00004264935,0.00001914654,0.0001485058,0.0001427844,0.00009829039],"domain_scores_gemma":[0.9996439,0.00003950778,0.00002881531,0.0000914069,0.0001727671,0.00002348227],"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":[0.0005241188,0.0002912034,0.002839489,0.00009551842,0.0001256891,0.0001211387,0.00005461386,0.1448186,0.06667127,0.003228697,0.006418249,0.7748114],"study_design_scores_gemma":[0.00001062339,0.0001132968,0.001002792,0.000006447544,0.00002743089,0.00007694621,0.00001631489,0.9759743,0.01927414,0.00166771,0.001815065,0.00001500761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07128505,0.0003615982,0.9179951,0.000198849,0.00009980946,0.0001422696,0.0003333846,0.005231386,0.00435253],"genre_scores_gemma":[0.6576404,0.0003463104,0.3236482,0.0002109915,0.00009552061,0.0001157356,0.002538667,0.0003031525,0.01510112],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005058525,"threshold_uncertainty_score":0.01005816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009973096878613908,"score_gpt":0.2243675251961271,"score_spread":0.2143944283175132,"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."}}