{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002570478,0.0002216888,0.000442244,0.0001124082,0.0004440206,0.0001679544,0.00003796247,0.0001541956,0.00003789309],"category_scores_gemma":[0.00006034096,0.0001834934,0.00002337512,0.0001445672,0.0001478922,0.0001944767,0.00006892173,0.0001080267,3.700367e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001929693,"about_ca_system_score_gemma":0.00002908393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009053298,"about_ca_topic_score_gemma":0.0006967411,"domain_scores_codex":[0.9981464,0.000173089,0.000692256,0.0003330468,0.0003352352,0.0003200442],"domain_scores_gemma":[0.9992397,0.00004500058,0.000115636,0.0001551581,0.0003030076,0.0001415178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000643372,0.0008010461,0.3752823,0.01124929,0.0003071883,0.00002739085,0.00837011,0.3579279,0.01533488,0.003035772,0.2095908,0.01743002],"study_design_scores_gemma":[0.004131249,0.0002990746,0.05598425,0.0008945629,0.0002572983,0.00004789839,0.0005574811,0.8883001,0.002114116,0.001000141,0.0456145,0.0007993939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9569429,0.007803442,0.03225996,0.000398083,0.0006310539,0.0009241444,0.0002095508,0.0001727052,0.0006582105],"genre_scores_gemma":[0.9925733,0.005096881,0.001625795,0.00002075204,0.0002869834,0.00002628815,0.00008943855,0.00002568352,0.0002549195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5303722,"threshold_uncertainty_score":0.7482644,"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."}}