{"id":"W2468494510","doi":"10.1061/9780784479957.028","title":"Application of Neural Networks in Predicting the Remaining Useful Life of Water Pipelines","year":2016,"lang":"en","type":"article","venue":"Pipelines 2016","topic":"Water Systems and Optimization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Pipeline transport; Robustness (evolution); Artificial neural network; Breakage; Computer science; Engineering; Deep water; Reliability engineering; Operations research; Marine engineering; Artificial intelligence; Environmental engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005884293,0.0006334221,0.0004342926,0.0005845699,0.0002791617,0.0006092225,0.0005020741,0.0007124304,0.0006714954],"category_scores_gemma":[0.002125192,0.0002641764,0.0003782366,0.0005344783,0.0002282991,0.0005866267,0.0003368923,0.0005262087,0.00009984763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009802282,"about_ca_system_score_gemma":0.0007818365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04645345,"about_ca_topic_score_gemma":0.02758402,"domain_scores_codex":[0.9997975,0.00006148141,0.00001653295,0.00004642431,0.00004588043,0.00003211975],"domain_scores_gemma":[0.9993333,0.0004362939,0.00006748072,0.00002010162,0.0001238874,0.00001896483],"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":[0.00001954748,0.00002043727,0.00211355,0.000009307382,0.000009146285,0.00002362865,0.000006060369,0.9896293,0.0001543786,0.0001785016,0.0001112764,0.00772479],"study_design_scores_gemma":[4.238046e-7,0.000003826035,0.0002158657,9.375905e-7,0.000001059517,9.721896e-7,0.000001809595,0.9996176,0.00006049356,0.00007801854,0.00001802402,9.19518e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7197417,0.001469056,0.270109,0.0007150595,0.0001314171,0.00008782504,0.0005529605,0.0005228206,0.00667014],"genre_scores_gemma":[0.9908891,0.0002298936,0.007754117,0.00002364768,0.00001284048,0.00002654406,0.0001404844,0.000006584004,0.0009166755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04645345,"threshold_uncertainty_score":0.0923661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009160561232686253,"score_gpt":0.1949837572634584,"score_spread":0.1858231960307722,"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."}}