{"id":"W4313307496","doi":"10.1109/iceccme55909.2022.9987998","title":"A Deep Learning-Based Approach for Pipeline Cracks Monitoring","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Recall rate; Artificial intelligence; Pipeline (software); Computer science; Convolutional neural network; Frame rate; Frame (networking); F1 score; Pattern recognition (psychology); Computer vision; Artificial neural network; Precision and recall; Image (mathematics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002969404,0.0002794965,0.000252107,0.0003478494,0.0004518188,0.0001384824,0.0009964827,0.0000762652,0.00003305056],"category_scores_gemma":[0.00003223051,0.0003250678,0.0001120298,0.0002918809,0.00002487334,0.0001033408,0.0003016287,0.001055839,0.000001767503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004176816,"about_ca_system_score_gemma":0.00005980051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008006655,"about_ca_topic_score_gemma":7.738973e-7,"domain_scores_codex":[0.9985383,0.00004801917,0.0003534781,0.0003190619,0.000321903,0.0004192021],"domain_scores_gemma":[0.9989131,0.000192965,0.00008385257,0.0005356104,0.0001731418,0.0001013224],"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":[0.00002446567,0.00008309133,0.00004813703,0.00003029425,0.00008014718,9.338299e-7,0.0001133155,0.9336392,0.001847039,0.03413433,0.0002365285,0.02976258],"study_design_scores_gemma":[0.0005639504,0.0002246737,0.00003193499,0.000023144,0.0000208522,0.00001090912,0.00008525159,0.9674921,0.0002876881,0.0002302061,0.03071613,0.0003131361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002186914,0.001246943,0.9936377,0.0002560772,0.001060226,0.0003683016,0.00002949053,0.0004408501,0.0007735208],"genre_scores_gemma":[0.9286884,0.000995328,0.06897532,0.00004850982,0.0003103104,0.0005642691,0.000249869,0.00006653604,0.0001014273],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9265015,"threshold_uncertainty_score":0.9999201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01754051904263328,"score_gpt":0.2442048561873501,"score_spread":0.2266643371447168,"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."}}