{"id":"W4285592441","doi":"10.1002/cjce.24552","title":"Recovery of valeric acid using green solvents","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Diluent; Chemistry; Sunflower oil; Sunflower; Soybean oil; Extraction (chemistry); Stoichiometry; Exothermic reaction; Valeric acid; Dilution; Enthalpy; Partition coefficient; Nuclear chemistry; Chromatography; Organic chemistry; Acetic acid; Food science; Agronomy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001564345,0.00006882532,0.0001232074,0.0001302298,0.00005452527,0.00001373433,0.0001902464,0.00002498954,0.0001804975],"category_scores_gemma":[0.00003969892,0.00006423492,0.000068762,0.0002126807,0.00001475055,0.0001007591,0.000009246335,0.0003057561,8.937266e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002038843,"about_ca_system_score_gemma":0.0001589593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002011363,"about_ca_topic_score_gemma":0.00003150713,"domain_scores_codex":[0.9994206,0.00000750897,0.0002504656,0.00003461327,0.0001481573,0.0001386868],"domain_scores_gemma":[0.9996503,0.00002789716,0.00006507889,0.0000711847,0.00004647783,0.000139056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003792905,0.000002491167,0.00002811931,0.0000322251,0.00003842684,0.000009059186,0.0001673139,0.7129665,0.2859693,0.00003630835,0.000392589,0.00035386],"study_design_scores_gemma":[0.0005358484,0.00007170367,0.0001201281,0.00009136289,0.00008451656,0.001004608,0.0001366248,0.3556708,0.6166615,0.0004290994,0.02481009,0.0003836608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9932216,0.001356849,0.003951144,0.0001334809,0.0007430064,0.00004450537,0.00001250903,0.00002185379,0.0005150127],"genre_scores_gemma":[0.9994647,0.000003671813,0.0003617071,0.00003009196,0.00008487047,0.00000120028,0.000001062546,0.00001701108,0.00003568993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3572957,"threshold_uncertainty_score":0.2619424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01587495965250853,"score_gpt":0.2058709944703419,"score_spread":0.1899960348178333,"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."}}