{"id":"W3000110218","doi":"10.1155/2020/6412562","title":"Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Pixel; Deep learning; Noise (video); Process (computing); Pattern recognition (psychology); Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003625068,0.0007786014,0.0005172298,0.001049572,0.0002030853,0.0004346916,0.0008742128,0.0006220962,0.001268501],"category_scores_gemma":[0.0008266254,0.0002817129,0.0005633114,0.0004279093,0.0002674986,0.001034275,0.0007341152,0.000609722,0.0004158726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006401654,"about_ca_system_score_gemma":0.0004935982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0100123,"about_ca_topic_score_gemma":0.01507041,"domain_scores_codex":[0.9996985,0.0000256224,0.00001395636,0.0001127269,0.00008880992,0.00006039481],"domain_scores_gemma":[0.9996701,0.00007552038,0.00005414051,0.00007875662,0.00009722576,0.00002419218],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004515769,0.0004432374,0.01518525,0.0001221634,0.0002059222,0.0002142018,0.00007899824,0.2654608,0.09538018,0.001577583,0.005838655,0.6150414],"study_design_scores_gemma":[0.000007948975,0.00003247575,0.003554542,0.000004880195,0.00001728918,0.00002917998,0.000006802906,0.9836925,0.01176263,0.0003916425,0.0004923617,0.000007703036],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5494055,0.0009693106,0.437245,0.0002587269,0.00006779231,0.0001003066,0.001113674,0.007380841,0.003459036],"genre_scores_gemma":[0.9164921,0.0001984793,0.07837612,0.00008240396,0.00002110995,0.00003025996,0.002214404,0.00007310298,0.002512127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0100123,"threshold_uncertainty_score":0.01990807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007317009610048299,"score_gpt":0.2297082347808916,"score_spread":0.2223912251708433,"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."}}