{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003805923,0.000188846,0.0001640348,0.0001203416,0.0001501748,0.00004468473,0.00006586088,0.0001478713,0.00001470924],"category_scores_gemma":[0.000001622502,0.0001893642,0.00006806204,0.0002345761,0.00005515192,0.0003037778,7.396284e-7,0.0004594505,9.286553e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006180827,"about_ca_system_score_gemma":0.000002415029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000159669,"about_ca_topic_score_gemma":0.000009048106,"domain_scores_codex":[0.9992219,0.00002331478,0.0001896397,0.0001950411,0.0001231346,0.0002470137],"domain_scores_gemma":[0.9996013,0.00007036533,0.00005299684,0.0001699715,0.00004234446,0.00006304153],"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":[0.00001498417,0.00002100921,0.000021855,0.00002410326,0.00003119537,0.0000057101,0.00004656046,0.7641197,0.04146815,0.000006129834,0.0003312304,0.1939093],"study_design_scores_gemma":[0.0002396308,0.00005656478,0.001212543,0.00004046792,0.00005200262,0.00007132105,0.00006548648,0.9873738,0.01059289,0.00003433371,0.00008861246,0.0001724052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.125668,0.0003622017,0.8711923,0.00007981942,0.002166902,0.0001482727,0.00001003071,0.0001687291,0.0002036648],"genre_scores_gemma":[0.9958781,0.0004119639,0.003291449,0.00003075732,0.0002553488,0.000008113169,0.00000189485,0.00003949517,0.00008286502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8702101,"threshold_uncertainty_score":0.7722048,"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."}}