{"id":"W3011200270","doi":"10.1002/stc.2551","title":"CrackU‐net: A novel deep convolutional neural network for pixelwise pavement crack detection","year":2020,"lang":"en","type":"article","venue":"Structural Control and Health Monitoring","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":342,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Shaanxi Province","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Net (polyhedron); Pattern recognition (psychology); Artificial neural network; Mathematics; Geometry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001400166,0.0002816971,0.0003666798,0.00003800148,0.0004592305,0.0000740375,0.0001021832,0.00009296383,0.00000474853],"category_scores_gemma":[0.0000208629,0.0002625151,0.00008734607,0.0001119201,0.00003374718,0.0001981556,0.00002609776,0.0003072312,0.000001324778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000171512,"about_ca_system_score_gemma":0.00002934077,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005009661,"about_ca_topic_score_gemma":0.00002248321,"domain_scores_codex":[0.9983598,0.00001622957,0.0004181357,0.0003001413,0.0001799931,0.0007256736],"domain_scores_gemma":[0.9993408,0.00005764834,0.00009653722,0.0001026913,0.00008774032,0.0003145496],"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":[0.0006022199,0.000008978248,0.03951139,0.00169452,0.0003666484,0.000007722133,0.002404523,0.6858384,0.04857821,0.001286734,0.0005570588,0.2191436],"study_design_scores_gemma":[0.004432447,0.0004497999,0.1875861,0.0001064025,0.00005357274,0.00002403373,0.0003034004,0.7996711,0.00119207,0.0007096014,0.00488038,0.0005910777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7982649,0.007320361,0.1779033,0.002783348,0.01094964,0.001921439,0.0001074832,0.0007050647,0.00004449156],"genre_scores_gemma":[0.9887902,0.00006982742,0.003396573,0.0004743881,0.007122868,0.00008649438,0.00001053749,0.00004322039,0.000005960533],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2185525,"threshold_uncertainty_score":0.9999827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02187878553343378,"score_gpt":0.2610333528454232,"score_spread":0.2391545673119894,"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."}}