{"id":"W2476640782","doi":"10.1142/9789812702838_0100","title":"TRANSIENT STATE CONTROL IN PIPELINES USING GAS AND PARTICLE SWARM OPTIMIZATION","year":2004,"lang":"en","type":"book-chapter","venue":"WORLD SCIENTIFIC eBooks","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Transient (computer programming); Particle swarm optimization; Pipeline transport; Transient state; Particle (ecology); State (computer science); Control (management); Computer science; Control theory (sociology); Environmental science; Petroleum engineering; Engineering; Geology; Environmental engineering; Electrical engineering; Algorithm; Artificial intelligence; Oceanography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002826502,0.0005829514,0.0006633454,0.0002226162,0.0003323079,0.0007691651,0.0005309221,0.0007142686,0.001753155],"category_scores_gemma":[0.000763157,0.0003379622,0.0004633459,0.0004870254,0.000682155,0.0007385513,0.0004571939,0.0006951338,0.0001903238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005210554,"about_ca_system_score_gemma":0.0004268987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005752436,"about_ca_topic_score_gemma":0.004085421,"domain_scores_codex":[0.9999018,0.00002702587,0.000005802225,0.00002177097,0.00003311349,0.00001044369],"domain_scores_gemma":[0.9998637,0.00008812823,0.00001359553,0.000008821303,0.00002119303,0.000004485691],"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":[0.00004024274,0.0000148283,0.0000850802,0.00004639501,0.00001092791,0.0000196355,0.0000447024,0.9569622,0.001915892,0.009494882,0.0008928552,0.03047241],"study_design_scores_gemma":[0.000003600184,0.00001777818,0.00005270249,0.000003012619,0.000002605798,0.000003956005,0.000004885927,0.9954569,0.0003123417,0.003500673,0.000638491,0.000002983237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01153574,0.0007355144,0.9788659,0.0001411259,0.00008501014,0.00001820002,0.00002349367,0.000320314,0.008274645],"genre_scores_gemma":[0.8851618,0.001275852,0.09413049,0.00005908211,0.00009096506,0.0001280019,0.00008716097,0.0001345744,0.0189321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005752436,"threshold_uncertainty_score":0.01143789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01423075747312545,"score_gpt":0.208648484604002,"score_spread":0.1944177271308765,"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."}}