{"id":"W2145295358","doi":"10.1109/72.991425","title":"Classification of underground pipe scanned images using feature extraction and neuro-fuzzy algorithm","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Backpropagation; Computer science; Pipeline (software); Artificial intelligence; Feature extraction; Fuzzy logic; Preprocessor; Artificial neural network; Feature (linguistics); Pattern recognition (psychology); Neuro-fuzzy; Fuzzy set; Membership function; Fuzzy control system; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003315728,0.000330762,0.0003661666,0.0008686404,0.0001777429,0.0003591299,0.0004058064,0.0006809602,0.0007334624],"category_scores_gemma":[0.001310343,0.0001754987,0.0003243301,0.0004616039,0.000223521,0.0004145032,0.0001562129,0.0002485586,0.0002227436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004591649,"about_ca_system_score_gemma":0.0004229167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00439384,"about_ca_topic_score_gemma":0.003505595,"domain_scores_codex":[0.999837,0.00001892661,0.00001595368,0.00003122786,0.0000752477,0.00002157788],"domain_scores_gemma":[0.9995638,0.0001508096,0.00004431701,0.00002751672,0.0002006416,0.00001297536],"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":[0.0004979081,0.0002238301,0.005474614,0.0001406551,0.00005591656,0.0002714737,0.0001426429,0.1876497,0.09507084,0.001469521,0.001543409,0.7074594],"study_design_scores_gemma":[0.00001002147,0.00005195137,0.003405771,0.000008522893,0.0000126513,0.00005649528,0.00002301308,0.9839367,0.01162712,0.0004959932,0.0003616327,0.00001028294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2509058,0.0002009346,0.7456765,0.0001507916,0.00003430446,0.0001346157,0.0001576103,0.000827165,0.001912244],"genre_scores_gemma":[0.7318443,0.0001277897,0.265675,0.00003366192,0.00001542323,0.000137634,0.000231437,0.00001738347,0.001917415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00439384,"threshold_uncertainty_score":0.008736551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617301208147076,"score_gpt":0.2294689836415867,"score_spread":0.2132959715601159,"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."}}