{"id":"W2334944185","doi":"10.1115/ipc2002-27278","title":"Dynamic Simulation as a Tool for Optimizing Pressure Control Valve Performance","year":2002,"lang":"en","type":"article","venue":"4th International Pipeline Conference, Parts A and B","topic":"Hydraulic and Pneumatic Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Spinal Cord Injury BC","funders":"","keywords":"Control valves; Pipeline (software); Engineering; Piping; Flow control valve; Actuator; Process (computing); Range (aeronautics); Flow control (data); Dynamic simulation; Pressure control; Suction; Control theory (sociology); Simulation; Marine engineering; Computer science; Mechanical engineering; Control (management); Electrical 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":[],"consensus_categories":[],"category_scores_codex":[0.0001097675,0.0001367601,0.0001707662,0.00006345416,0.00005829306,0.00008643711,0.0001045675,0.00006531871,0.0003780234],"category_scores_gemma":[0.00005122195,0.0001236459,0.00005686264,0.00003222599,0.00002041308,0.0001764745,0.00001104521,0.00007228336,0.0000416298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001590078,"about_ca_system_score_gemma":0.000007698846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004881047,"about_ca_topic_score_gemma":0.000002519186,"domain_scores_codex":[0.9992414,0.00001106815,0.0002865201,0.0001494825,0.0001557962,0.0001557493],"domain_scores_gemma":[0.9995554,0.0001127823,0.00005314017,0.00009341582,0.0001360738,0.00004917045],"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":[0.00002975937,0.00003256226,0.0009897355,0.0001841911,0.0001854394,0.00000140537,0.000654839,0.9626852,0.0001943633,0.001325073,0.002876658,0.03084073],"study_design_scores_gemma":[0.0007740015,0.00003179019,0.0003096191,0.00008690074,0.00003134708,0.00000868493,0.00002324167,0.9309362,0.00002057368,0.0000960937,0.06753856,0.0001430165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1598494,0.0009355312,0.7952921,0.0005721578,0.00189672,0.001007449,0.0001816598,0.0003132848,0.03995166],"genre_scores_gemma":[0.9955444,0.0001153379,0.0004745968,0.00009646965,0.0001738828,0.00007277728,0.00003486983,0.00001610682,0.003471566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.835695,"threshold_uncertainty_score":0.5042132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0195006775846019,"score_gpt":0.2475136450764315,"score_spread":0.2280129674918296,"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."}}