{"id":"W4377042701","doi":"10.3390/s23104793","title":"Image Generation and Recognition for Railway Surface Defect Detection","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Transport Canada","keywords":"Obstacle; Artificial intelligence; Nondestructive testing; Artificial neural network; Segmentation; Computer science; Pattern recognition (psychology); Identification (biology); Track (disk drive); Pixel; Computer vision; Image segmentation; Sampling (signal processing); Filter (signal processing)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008079194,0.00006218492,0.00005293999,0.00004142202,0.00005968416,0.00002329299,0.00001183355,0.0000424569,0.000002616491],"category_scores_gemma":[0.00003116135,0.00006298078,0.00002709181,0.00008852635,0.000007678454,0.00006365708,0.000003902634,0.00004405046,0.00002439776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000178742,"about_ca_system_score_gemma":0.000001577807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005566176,"about_ca_topic_score_gemma":0.00001520576,"domain_scores_codex":[0.9996778,0.000006935114,0.0000653485,0.00008500601,0.00003562961,0.0001292726],"domain_scores_gemma":[0.9998723,0.00002514894,0.000009159856,0.00004597531,0.00002836886,0.00001902552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006881266,7.161935e-7,0.00006121269,0.00005807286,0.00001633375,0.000001861768,0.0002842782,0.02483256,0.9190631,0.000004062358,0.0008452421,0.0548257],"study_design_scores_gemma":[0.0002761179,0.00002856272,0.001759451,0.00001429787,0.00001715317,0.000007810317,0.0001504288,0.4171656,0.5783127,0.0003965131,0.001714062,0.0001572377],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928637,0.00002071597,0.00565583,0.0000110706,0.0007812104,0.0001337295,0.000008965178,0.0003116151,0.0002131566],"genre_scores_gemma":[0.9968723,0.00005115732,0.002566144,0.000005291879,0.0003668568,0.00001080906,0.00002650314,0.00002133748,0.00007962382],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3923331,"threshold_uncertainty_score":0.2568282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01622693294988104,"score_gpt":0.2228881358327438,"score_spread":0.2066612028828628,"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."}}