{"id":"W2136260484","doi":"10.1109/wac.2002.1049540","title":"Modeling of supercritical ethane extraction by artificial neural networks","year":2003,"lang":"en","type":"article","venue":"","topic":"Analytical Chemistry and Chromatography","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Guelph","funders":"","keywords":"Artificial neural network; Supercritical fluid; Equation of state; Supercritical fluid extraction; Nonlinear system; Computer science; Binary number; Extraction (chemistry); Mass transfer; Artificial intelligence; Biological system; Thermodynamics; Mathematics; Chemistry; Physics; Chromatography","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":[0.0002964595,0.0004612938,0.0003899174,0.0002764751,0.000285998,0.0004698215,0.0007537869,0.0007364359,0.0008645711],"category_scores_gemma":[0.0005913681,0.0002867955,0.0003759377,0.0003180975,0.0003065273,0.0009316235,0.000310477,0.0004684176,0.0002272205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006010587,"about_ca_system_score_gemma":0.0005289516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006722849,"about_ca_topic_score_gemma":0.005548109,"domain_scores_codex":[0.9998347,0.00003957842,0.00001076778,0.00003292567,0.00006496566,0.00001713621],"domain_scores_gemma":[0.9998177,0.00009100055,0.0000253038,0.000009993812,0.00005052227,0.000005510992],"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":[0.00002306293,0.0000128234,0.0002803503,0.00002532021,0.00001198513,0.00002400028,0.00001460895,0.9891592,0.003155948,0.001106411,0.00006996149,0.00611639],"study_design_scores_gemma":[7.707499e-7,0.000003599775,0.00005004047,7.578647e-7,0.000001271531,0.00000213814,8.935008e-7,0.999126,0.00055641,0.0001780182,0.00007865875,0.000001435716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1444088,0.0006726186,0.8478442,0.0001922721,0.00006077104,0.00006185156,0.0001255403,0.0004923819,0.006141516],"genre_scores_gemma":[0.9463757,0.0006417231,0.04656657,0.00004773852,0.00002318406,0.0001797008,0.0001368007,0.00003549206,0.005993065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006722849,"threshold_uncertainty_score":0.01336741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.017870194433334,"score_gpt":0.259876795830424,"score_spread":0.24200660139709,"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."}}