{"id":"W4401110452","doi":"10.1109/wfpst58552.2024.00029","title":"Leveraging Public Safety and Enhancing Crack Detection in Concrete Bridges using Deep Convolutional Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Convolutional neural network; Computer science; Deep learning; Artificial intelligence","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":[0.0003572197,0.0007445407,0.0003339063,0.0006334324,0.0001726427,0.0004363009,0.0006571678,0.0007990641,0.0008338427],"category_scores_gemma":[0.0007511263,0.0002365728,0.0004721115,0.0002816732,0.0002789063,0.0007097638,0.0006767446,0.0006955478,0.0003982254],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005029418,"about_ca_system_score_gemma":0.0005894593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01005524,"about_ca_topic_score_gemma":0.01834235,"domain_scores_codex":[0.9998323,0.00001708297,0.000005970358,0.00005624962,0.00004773293,0.00004078894],"domain_scores_gemma":[0.9997827,0.00005720827,0.00003214101,0.00002905335,0.00007955332,0.00001930152],"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":[0.0003691422,0.0004397619,0.01025988,0.0001359841,0.000155118,0.0002324776,0.0001431055,0.4512389,0.07421038,0.002103065,0.004881241,0.4558309],"study_design_scores_gemma":[0.000003056673,0.00003889742,0.001097539,0.000006252047,0.00001413558,0.00001780657,0.00001023635,0.992456,0.005547219,0.0004447829,0.0003593468,0.000004743487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5354345,0.0009514908,0.4531818,0.0004615057,0.0001166738,0.00006686697,0.0005218077,0.00378857,0.005476796],"genre_scores_gemma":[0.9408596,0.0002634162,0.05439689,0.0001334199,0.00003175747,0.00002346428,0.001031455,0.00006110331,0.003198982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01005524,"threshold_uncertainty_score":0.01999342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009341830521606058,"score_gpt":0.2108757722225449,"score_spread":0.2015339417009389,"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."}}