{"id":"W3168465972","doi":"10.1117/12.2592243","title":"Sequential concrete crack segmentation using deep fully convolutional neural networks and data fusion","year":2021,"lang":"en","type":"article","venue":"","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Segmentation; Deep learning; Artificial neural network; Pattern recognition (psychology); Encoder; Image segmentation; Computer vision","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":[],"consensus_categories":[],"category_scores_codex":[0.00006206414,0.00009798652,0.00009034607,0.00002068066,0.00008717239,0.00006466004,0.00007238347,0.00005712629,0.0001195145],"category_scores_gemma":[0.00001046309,0.00009777254,0.00001503317,0.00006971183,0.00002597903,0.0002941507,0.0001342935,0.000114114,0.000001361864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004529812,"about_ca_system_score_gemma":0.00001540652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001739746,"about_ca_topic_score_gemma":0.00001708774,"domain_scores_codex":[0.9993845,0.00001610194,0.0001378485,0.0001777155,0.000096852,0.0001869817],"domain_scores_gemma":[0.9996892,0.00001820904,0.00001799807,0.0001776249,0.00005130109,0.00004570209],"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":[0.00002240953,0.000001828382,0.004819994,0.00006737372,0.00008784354,0.00008488175,0.0001500532,0.7088033,0.2691858,0.0006105741,0.001180812,0.0149851],"study_design_scores_gemma":[0.0002646337,0.000005071727,0.001327318,0.00001320181,0.00002207838,0.00008585218,0.000151156,0.9937524,0.003877768,0.00003148391,0.0003494287,0.0001195991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6199843,0.0005549967,0.3774519,0.00002234071,0.001466432,0.00006340317,0.00001432288,0.00009592049,0.0003463959],"genre_scores_gemma":[0.9863762,0.00006254289,0.01254091,0.0000775193,0.0006176763,0.000001245731,0.0002759206,0.00001580709,0.00003213976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.366392,"threshold_uncertainty_score":0.3987049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02358224980192541,"score_gpt":0.2633450211037937,"score_spread":0.2397627713018683,"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."}}