{"id":"W1976295841","doi":"10.1016/j.advengsoft.2007.02.003","title":"A comparison of automation techniques for optimization of compressor scheduling","year":2007,"lang":"en","type":"article","venue":"Advances in Engineering Software","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automation; Compressor station; Gas compressor; Pipeline (software); Scheduling (production processes); Selection (genetic algorithm); Pipeline transport; Integer programming; Computer science; Natural gas; Decision support system; Task (project management); Operations research; Engineering; Systems engineering; Data mining; Machine learning; Operations management; Algorithm; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003537789,0.0001243471,0.0002926888,0.000287352,0.0000111632,0.000004449446,0.0001125336,0.00008981727,0.000002723382],"category_scores_gemma":[0.0003303613,0.0001438509,0.00004964643,0.0003136334,0.00001361935,0.0002338982,0.00001077607,0.00009763348,8.910434e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004493349,"about_ca_system_score_gemma":0.000004784405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001154434,"about_ca_topic_score_gemma":0.00000120639,"domain_scores_codex":[0.9990784,0.000006174335,0.0005178574,0.00009981685,0.0001218832,0.000175892],"domain_scores_gemma":[0.9993184,0.0003518984,0.00007275615,0.0001449186,0.00008336428,0.00002864139],"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":[0.000009720031,0.00001515398,0.002781097,0.001190492,0.000008044252,1.605456e-7,0.0001333392,0.9831693,0.001853377,0.0001501771,0.000001931537,0.01068728],"study_design_scores_gemma":[0.0002311451,0.00003028726,0.0004550667,0.0002420692,0.000005315118,3.724907e-7,0.00002039305,0.932938,0.06534784,0.00004550874,0.0005618861,0.0001220626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02402805,0.002496343,0.9724469,0.000001206696,0.0002678598,0.0002214382,0.000007678956,0.0004893338,0.00004115986],"genre_scores_gemma":[0.4619421,0.00005685387,0.537935,3.957423e-7,0.00001835035,0.00001427096,0.00001031587,0.00002106074,0.000001629862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4379141,"threshold_uncertainty_score":0.5866072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531781218294413,"score_gpt":0.3351802974988345,"score_spread":0.3198624853158904,"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."}}