{"id":"W4321599936","doi":"10.3390/rs15051218","title":"Leveraging Saliency in Single-Stage Multi-Label Concrete Defect Detection Using Unmanned Aerial Vehicle Imagery","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Université du Québec en Outaouais","funders":"National Research Council Canada","keywords":"Computer science; Artificial intelligence; Residual; Computer vision; Pattern recognition (psychology); Feature (linguistics); Bridge (graph theory); Pooling; Feature extraction; Detector","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003310216,0.0006184764,0.0004672351,0.0008153873,0.0001573723,0.0003663588,0.0007380391,0.0004473617,0.0006571891],"category_scores_gemma":[0.0006971466,0.0002146107,0.0004375542,0.000297531,0.0002631048,0.0007784468,0.000549159,0.0003145081,0.0002598193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003688327,"about_ca_system_score_gemma":0.0003451742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002821974,"about_ca_topic_score_gemma":0.00592766,"domain_scores_codex":[0.9998454,0.00001564149,0.000004887691,0.00005085262,0.00005499586,0.00002831852],"domain_scores_gemma":[0.9996918,0.000097679,0.00004893136,0.00003685883,0.00009871623,0.00002599746],"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":[0.0004561989,0.000223558,0.006086552,0.000206974,0.000129775,0.0003783584,0.0001600896,0.1894451,0.1749667,0.001858435,0.002205949,0.6238823],"study_design_scores_gemma":[0.000006814984,0.0001189478,0.00284934,0.000005119334,0.00002002768,0.0001371643,0.00001765206,0.9765578,0.01897348,0.0007421904,0.0005629831,0.000008552619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1461547,0.0003451089,0.8504956,0.00007867852,0.00005193573,0.00007633738,0.00009145863,0.001371629,0.001334586],"genre_scores_gemma":[0.8666645,0.0001485102,0.1312082,0.00005820086,0.00003568294,0.00003791604,0.0001953502,0.00005531449,0.001596292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002821974,"threshold_uncertainty_score":0.005611122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03441999319474875,"score_gpt":0.2543256944988,"score_spread":0.2199057013040513,"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."}}