{"id":"W4309708571","doi":"10.3390/electronics11223769","title":"A New Energy-Aware Method for Gas Lift Allocation in IoT-Based Industries Using a Chemical Reaction-Based Optimization Algorithm","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Gas lift; Computer science; Flexibility (engineering); Fossil fuel; Process engineering; Lift (data mining); Rate of convergence; Production (economics); Energy supply; Petrochemical; Mathematical optimization; Algorithm; Environmental science; Engineering; Petroleum engineering; Energy (signal processing); Environmental engineering; Waste management; Mathematics; Channel (broadcasting); Telecommunications; Data mining","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":[0.0004504162,0.0007864314,0.00093978,0.0005453845,0.0004670502,0.0007067453,0.001049705,0.0009784689,0.002597155],"category_scores_gemma":[0.0006600655,0.0003906892,0.001001555,0.0005478964,0.0004033612,0.0006360612,0.0007491651,0.0007394711,0.0003650773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004197595,"about_ca_system_score_gemma":0.001028624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003929029,"about_ca_topic_score_gemma":0.003303333,"domain_scores_codex":[0.9998149,0.00004373361,0.00001130527,0.00004491929,0.00005769528,0.0000274439],"domain_scores_gemma":[0.9998335,0.00007784853,0.00002212197,0.000009611163,0.00004338042,0.00001346793],"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":[0.0000531329,0.00004542739,0.0004448492,0.00007421461,0.00004489199,0.00006518982,0.00002996177,0.9469329,0.002957094,0.005278183,0.001154445,0.04291966],"study_design_scores_gemma":[0.000007039654,0.00001117963,0.00003254501,0.00000273327,0.000003792187,0.000007423155,0.000003060388,0.9989622,0.0001701464,0.0004125941,0.0003851999,0.000002126465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008674487,0.0003055921,0.9869986,0.0001401433,0.00007017889,0.00005222588,0.00002322014,0.000213035,0.00352251],"genre_scores_gemma":[0.3957866,0.0005212627,0.5954629,0.0002553995,0.0001027773,0.0004901019,0.0001781943,0.0001610825,0.007041595],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003929029,"threshold_uncertainty_score":0.008688331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01130054695684747,"score_gpt":0.2343440078441919,"score_spread":0.2230434608873444,"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."}}