{"id":"W1984622512","doi":"10.1115/ipc2006-10007","title":"Large Pipeline Network Optimization: Summary and Conclusions of TransCanada Research Effort","year":2006,"lang":"en","type":"article","venue":"Volume 3: Materials and Joining; Pipeline Automation and Measurement; Risk and Reliability, Parts A and B","topic":"Water Systems and Optimization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"TransCanada (Canada); Nova Chemicals (Canada)","funders":"","keywords":"Pipeline (software); Computer science; Optimization problem; Block (permutation group theory); Mathematical optimization; Range (aeronautics); Compressor station; Stochastic optimization; Gas compressor; Stability (learning theory); Set (abstract data type); Convergence (economics); Engineering; Algorithm","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":[0.001781144,0.0006316247,0.0006952497,0.0009495532,0.0003796112,0.001342544,0.0009303666,0.0005609291,0.003403533],"category_scores_gemma":[0.002555199,0.0001786867,0.0005794463,0.001477105,0.000459635,0.001000984,0.0007615627,0.0008012922,0.0005577623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001653878,"about_ca_system_score_gemma":0.001513311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01016219,"about_ca_topic_score_gemma":0.008500457,"domain_scores_codex":[0.999406,0.0001603101,0.00003425727,0.0001253276,0.0002260987,0.000047965],"domain_scores_gemma":[0.9988343,0.0004552288,0.0000421748,0.0001031813,0.0005063812,0.00005871406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005453053,0.0004050754,0.00315462,0.001608434,0.0001296177,0.0004136813,0.0002579321,0.2935845,0.008224549,0.03601619,0.01572278,0.6399373],"study_design_scores_gemma":[0.0001880797,0.001111987,0.01034653,0.001190133,0.000200692,0.000609308,0.000756587,0.6770213,0.02666803,0.03461282,0.2471435,0.000151125],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.305944,0.1811167,0.2448418,0.01543313,0.001282985,0.0002809593,0.0008100367,0.0009905336,0.2493],"genre_scores_gemma":[0.6569297,0.1073567,0.1864651,0.001353623,0.0007039016,0.000175587,0.001750838,0.0004671722,0.04479733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01016219,"threshold_uncertainty_score":0.02020609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162692754437059,"score_gpt":0.2283428273752872,"score_spread":0.2120735519315813,"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."}}