{"id":"W3171891874","doi":"10.1016/j.chroma.2021.462330","title":"Advances in Automated Piston Liquid-Liquid Microextraction Technique","year":2021,"lang":"en","type":"article","venue":"Journal of Chromatography A","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dow Chemical (Canada)","funders":"University of Tasmania; Dow Chemical Company","keywords":"Repeatability; Chromatography; Chemistry; Extraction (chemistry); Emulsion; Sample preparation; Solvent; Sonication; Aqueous solution; Demulsifier; Distillation; Process engineering; Liquid–liquid extraction; Organic chemistry","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":[0.001050098,0.0007099027,0.0004974201,0.001119391,0.0003916119,0.0008649634,0.001069628,0.0007972821,0.002036545],"category_scores_gemma":[0.0007249259,0.0004023007,0.0003750349,0.0009788773,0.0003785477,0.001412466,0.0006515373,0.001277238,0.001912486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004605144,"about_ca_system_score_gemma":0.0008595387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006233453,"about_ca_topic_score_gemma":0.001590525,"domain_scores_codex":[0.9983212,0.0001725599,0.00006783033,0.0003156532,0.001070418,0.00005235836],"domain_scores_gemma":[0.9991739,0.0002471277,0.00008609047,0.00008608949,0.0003812208,0.00002543524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001068118,0.0001379103,0.001570347,0.0006313769,0.00006034476,0.0001628232,0.00008368944,0.0005649277,0.8026644,0.003227153,0.002708611,0.1880815],"study_design_scores_gemma":[0.00002129818,0.0002798575,0.004777667,0.0000407117,0.00008658937,0.0008140493,0.00005891433,0.0105607,0.8754393,0.00189082,0.1059693,0.00006078458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.11892,0.04021809,0.812656,0.002092017,0.001837253,0.0003286194,0.001065442,0.002151096,0.02073143],"genre_scores_gemma":[0.3218718,0.03516764,0.602713,0.001515968,0.001211611,0.0003393386,0.001844392,0.0001986211,0.03513773],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002036545,"threshold_uncertainty_score":0.00681293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01028860677381558,"score_gpt":0.2983133448770574,"score_spread":0.2880247381032418,"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."}}