{"id":"W4251383964","doi":"10.1515/iupac.88.0240","title":"Dispersive Liquid–Liquid Microextraction (DLLME)","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Extraction (chemistry); Computer science; Chromatography; Liquid liquid; Process engineering; Sample (material); Microwave; Sample preparation; Chemistry; Engineering","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":[0.003682389,0.002139481,0.002145646,0.005480696,0.0008594303,0.002557727,0.003180159,0.001519037,0.02452999],"category_scores_gemma":[0.01176384,0.0005834364,0.001730803,0.009062374,0.0005081008,0.001511681,0.002518999,0.002231767,0.03272612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001462474,"about_ca_system_score_gemma":0.003798382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009111687,"about_ca_topic_score_gemma":0.02151856,"domain_scores_codex":[0.9969138,0.0007027846,0.0005525033,0.0009823001,0.0006191728,0.0002294471],"domain_scores_gemma":[0.9951805,0.001966827,0.0008680874,0.000884781,0.0008983313,0.0002016156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003668637,0.00007907659,0.007258934,0.01616088,0.0005135192,0.0001084353,0.00008846822,0.001267681,0.001784684,0.00260905,0.9400561,0.02970632],"study_design_scores_gemma":[0.0001996478,0.00003840575,0.00743233,0.001251076,0.0001457859,0.0001014474,0.00006628139,0.0004405892,0.001244328,0.002239188,0.9867992,0.00004179928],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002945819,0.0004588073,0.0004823862,0.0000910156,0.00003277496,0.00003801563,0.9976149,0.0003246111,0.0006630758],"genre_scores_gemma":[0.0005171525,0.0004274065,0.001685748,0.00008607347,0.000008430033,0.0002263767,0.9965464,0.00007608812,0.0004263267],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02452999,"threshold_uncertainty_score":0.08206099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02391514177214095,"score_gpt":0.4304476308960726,"score_spread":0.4065324891239316,"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."}}