{"id":"W2908623137","doi":"10.1002/aic.16532","title":"Long range pipeline leak detection and localization using discrete observer and support vector machine","year":2019,"lang":"en","type":"article","venue":"AIChE Journal","topic":"Water Systems and Optimization","field":"Engineering","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Discretization; Control theory (sociology); Linearization; Observer (physics); Mathematics; Partial differential equation; Nonlinear system; State vector; Applied mathematics; Computer science; Mathematical analysis; Physics; Artificial intelligence","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":[0.0001804705,0.00009399988,0.0001175796,0.0000623686,0.00006816538,0.00009174478,0.00002673347,0.00006074466,0.00005194071],"category_scores_gemma":[0.000007028093,0.00008005573,0.00001969978,0.00007180745,0.000008169109,0.0003439891,0.00001429192,0.0001250205,0.000005031942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003809737,"about_ca_system_score_gemma":0.000005340138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002918901,"about_ca_topic_score_gemma":0.000273862,"domain_scores_codex":[0.9994904,0.00002427057,0.0001897517,0.00008193061,0.00009455932,0.0001190993],"domain_scores_gemma":[0.9997823,0.000007555836,0.0000475384,0.00006059105,0.0000375651,0.00006443457],"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":[0.00005210444,0.00001191014,0.3936029,0.0003380553,0.00007998162,0.00002238807,0.0009070999,0.589204,0.01149468,0.00001245588,0.0003814241,0.003893017],"study_design_scores_gemma":[0.0006140285,0.00004169242,0.04280912,0.00005719444,0.00003122201,0.0003534303,0.00002524921,0.9542075,0.001009129,0.00001099047,0.0007098593,0.0001305295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5778088,0.0006497394,0.4207283,0.00001633156,0.000516918,0.0001010246,0.000002404345,0.00003963326,0.0001368887],"genre_scores_gemma":[0.9989721,0.0001366919,0.0003156542,0.00001792833,0.0001944141,6.230969e-7,0.00000539505,0.00002451563,0.000332635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4211634,"threshold_uncertainty_score":0.3264578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009781508912874937,"score_gpt":0.2027032356732275,"score_spread":0.1929217267603526,"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."}}