{"id":"W2170663864","doi":"10.1002/cjce.20175","title":"Automatic design of conventional distillation column sequence by genetic algorithm","year":2009,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Process Optimization and Integration","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Distillation; Benchmark (surveying); Sequence (biology); Column (typography); Fractionating column; Computer science; Process (computing); Genetic algorithm; Algorithm; Field (mathematics); Mathematical optimization; Mathematics; Machine learning; Chemistry; Chromatography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0004286273,0.0006698968,0.0007093442,0.0006666646,0.000288528,0.0005148521,0.0006544015,0.0005676998,0.0022648],"category_scores_gemma":[0.0007506176,0.0004020582,0.0004036248,0.0004719037,0.0003549579,0.0003311927,0.0002947932,0.0005362008,0.000308981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004980951,"about_ca_system_score_gemma":0.001421572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002927685,"about_ca_topic_score_gemma":0.003676341,"domain_scores_codex":[0.9997724,0.00004435024,0.00001068331,0.00006045649,0.00007520538,0.0000369419],"domain_scores_gemma":[0.999671,0.0001557994,0.00004465708,0.00001890308,0.0000927287,0.00001691271],"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":[0.0001980372,0.0001659874,0.0008156443,0.0002190653,0.00004628376,0.00008912465,0.00007425007,0.7163397,0.05086116,0.005652175,0.001246406,0.2242921],"study_design_scores_gemma":[0.00004819372,0.0001048747,0.000221588,0.000007712848,0.00001713521,0.00002289785,0.00001015441,0.9903235,0.007157945,0.0009914488,0.001087414,0.000007196299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07989687,0.0002727133,0.9142396,0.00008933123,0.00003920136,0.0001490103,0.00006150475,0.0009195116,0.004332368],"genre_scores_gemma":[0.4124987,0.0001293686,0.584985,0.00006626408,0.00001312245,0.0002195085,0.0001386426,0.0000667866,0.001882704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002927685,"threshold_uncertainty_score":0.007576525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00868909676077503,"score_gpt":0.1886200491726994,"score_spread":0.1799309524119244,"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."}}