{"id":"W4394577748","doi":"10.1080/00986445.2024.2336234","title":"A review of the application of Density Functional Theory and machine learning for oxidative coupling of methane reaction for ethylene production","year":2024,"lang":"en","type":"review","venue":"Chemical Engineering Communications","topic":"Catalysis and Oxidation Reactions","field":"Chemical Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; University of Regina","keywords":"Oxidative coupling of methane; Ethylene; Methane; Density functional theory; Coupling (piping); Oxidative phosphorylation; Production (economics); Chemistry; Computational chemistry; Biochemical engineering; Organic chemistry; Chemical engineering; Engineering; Catalysis; Mechanical engineering; Biochemistry; Economics","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":[0.0008681252,0.0002318399,0.0008998351,0.0001356029,0.00004799226,0.000004251709,0.0003352404,0.0001833105,0.000002031266],"category_scores_gemma":[0.002647893,0.0001846639,0.0005356409,0.0005505705,0.0001049163,0.00005572093,0.000200071,0.0004996696,5.125962e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001055712,"about_ca_system_score_gemma":0.00007056173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001159642,"about_ca_topic_score_gemma":0.000001069975,"domain_scores_codex":[0.9985933,0.00003082905,0.0008654897,0.0002658903,0.0001350513,0.0001094065],"domain_scores_gemma":[0.9965069,0.001575737,0.000639042,0.0008789828,0.0003629412,0.00003636117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002888342,0.0001803531,0.000001165095,0.2764759,0.001101165,1.075901e-8,0.00008512881,0.001412414,0.5806056,0.02763731,0.000123666,0.1123484],"study_design_scores_gemma":[0.0003611559,0.0000360306,0.000003297904,0.0906406,0.01009744,0.0000265507,0.00003640914,0.08768602,0.2007543,0.0004236326,0.6093049,0.0006296566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005814701,0.9105575,0.08782517,0.00008802368,0.000094018,0.001184543,0.0001313202,0.00005283174,0.000008444077],"genre_scores_gemma":[0.003352581,0.9911609,0.003086776,0.000004297536,0.00006834667,0.0008817276,0.001335326,0.00005674711,0.00005335499],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.6091812,"threshold_uncertainty_score":0.7530376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04834081054673036,"score_gpt":0.3190861386689302,"score_spread":0.2707453281221998,"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."}}