{"id":"W3033645921","doi":"10.1016/j.autcon.2020.103291","title":"Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning","year":2020,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":395,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Research Manitoba","keywords":"Pixel; Intersection (aeronautics); Segmentation; Convolutional neural network; Artificial intelligence; Computer science; Algorithm; Bounding overwatch; Computer vision; Pattern recognition (psychology); Engineering","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":[0.000129095,0.0001412358,0.0001510866,0.00007229657,0.0001605639,0.0001148957,0.00004813924,0.00006536765,0.00002413161],"category_scores_gemma":[0.00004383632,0.0001683425,0.00002311853,0.0001909799,0.00006800306,0.000553097,0.00001916555,0.0001862198,0.000009680847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001389584,"about_ca_system_score_gemma":0.00001010256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001896438,"about_ca_topic_score_gemma":0.000005866987,"domain_scores_codex":[0.9990774,0.00004418835,0.0003443401,0.0002009189,0.0001333521,0.000199741],"domain_scores_gemma":[0.9996684,0.00003510869,0.0001053386,0.00007350155,0.00006643268,0.00005125605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002798162,0.000001868837,0.03445433,0.000255988,0.00003855279,0.000003482284,0.00441276,0.1760335,0.625845,0.0008902248,0.00003333947,0.158003],"study_design_scores_gemma":[0.0005198207,0.00001881578,0.04345019,0.00003984019,0.00001133395,0.00005869232,0.002651729,0.9349921,0.01762369,0.0001447773,0.0002873858,0.0002016327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.723825,0.00003040174,0.2752559,0.00004027022,0.0003932704,0.0001306423,0.000005951572,0.0002356784,0.00008286444],"genre_scores_gemma":[0.9701324,0.00003426202,0.02953205,0.00003020221,0.0001615908,0.00001070472,0.0000737366,0.00002280842,0.000002310051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7589586,"threshold_uncertainty_score":0.6864807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04331559413787201,"score_gpt":0.2807973856158583,"score_spread":0.2374817914779863,"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."}}