{"id":"W2071161986","doi":"10.1002/cjce.20212","title":"A neural network approach to predict activity coefficients","year":2009,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Process Optimization and Integration","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"UNIFAC; Artificial neural network; Group contribution method; Binary number; Activity coefficient; Representation (politics); Computer science; Mathematics; Phase (matter); Artificial intelligence; Phase equilibrium; Chemistry; Organic chemistry; Arithmetic","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001164211,0.0008256386,0.0006191042,0.001460115,0.0003237904,0.0007084122,0.0006471986,0.0007194912,0.002343265],"category_scores_gemma":[0.003052053,0.0003928092,0.000514527,0.000980621,0.0002590426,0.0007109667,0.0003676386,0.0009159445,0.0006143493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009784208,"about_ca_system_score_gemma":0.0006631084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006573809,"about_ca_topic_score_gemma":0.004884596,"domain_scores_codex":[0.9996557,0.00009389587,0.00002580475,0.00007313922,0.0001264132,0.00002492305],"domain_scores_gemma":[0.9991704,0.0004798268,0.00006116723,0.00003140217,0.0002409216,0.00001619351],"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":[0.00008964803,0.0001101053,0.001774813,0.00008918039,0.00005965704,0.00004358887,0.00002408359,0.8768726,0.006298762,0.001918288,0.0007393403,0.1119798],"study_design_scores_gemma":[0.000001861974,0.000007111721,0.0001424833,0.000002555504,0.000002933191,0.00000262602,0.000001183805,0.9985298,0.0008856117,0.0002916705,0.0001295035,0.000002636946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.103976,0.0005740605,0.8887965,0.0002578384,0.00009395361,0.0001152359,0.0003104862,0.001267602,0.00460843],"genre_scores_gemma":[0.8132502,0.0003719403,0.1810867,0.00007680043,0.00004355942,0.0002901203,0.000346584,0.00008252003,0.004451645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006573809,"threshold_uncertainty_score":0.01307106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007308879783958363,"score_gpt":0.1795097424207537,"score_spread":0.1722008626367953,"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."}}