{"id":"W4392841530","doi":"10.2139/ssrn.4730270","title":"Moving Horizon Estimation for Pipeline Leak Detection, Localization, and Constrained Size Estimation","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Water Systems and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Estimation; Horizon; Leak detection; Pipeline (software); Moving horizon estimation; Computer science; Leak; Econometrics; Statistics; Mathematics; Environmental science; Economics; Artificial intelligence; Kalman filter; Environmental engineering","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":[0.0009892025,0.0003164874,0.0003062207,0.0002255917,0.000192519,0.0003479623,0.0001122638,0.0003156695,0.00000481456],"category_scores_gemma":[0.000181403,0.0003177212,0.0001011379,0.0001535235,0.00002779374,0.0001761834,0.00006041582,0.001415081,0.000005559495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009659082,"about_ca_system_score_gemma":0.0005267944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003238046,"about_ca_topic_score_gemma":0.001561378,"domain_scores_codex":[0.9979997,0.00003936894,0.0006183477,0.0002918183,0.0001909085,0.0008598723],"domain_scores_gemma":[0.9993255,0.00006898121,0.0001736323,0.0001492082,0.0002109088,0.00007180271],"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":[0.00001304128,0.000006292468,0.000006422991,0.0005532833,0.0001311749,6.949811e-7,0.0001703365,0.9527138,0.0001026164,0.00398162,0.0002594845,0.04206125],"study_design_scores_gemma":[0.0003479399,0.00009325942,0.000005507565,0.0002596812,0.000137622,0.0001843331,0.0001271334,0.8388876,0.0003129494,0.1591939,0.0001904632,0.0002595644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004826264,0.003767631,0.9886746,0.0001621018,0.001469603,0.000577666,0.00001944334,0.0003453398,0.0001573786],"genre_scores_gemma":[0.9932037,0.001226419,0.004119609,0.000008508991,0.0006172682,0.00006969633,0.00009753373,0.0001031297,0.0005541236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9883775,"threshold_uncertainty_score":0.9999275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004821435113089459,"score_gpt":0.2108868863778076,"score_spread":0.2060654512647181,"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."}}