{"id":"W4256491651","doi":"10.1515/iupac.88.0291","title":"Solvent Desorption","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Process Optimization and Integration","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Extraction (chemistry); Computer science; Process engineering; Solvent extraction; Sample preparation; Sample (material); Chromatography; Biochemical engineering; 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":[0.002299548,0.002791811,0.002128484,0.00395914,0.001126966,0.002452606,0.002679878,0.00179117,0.03667381],"category_scores_gemma":[0.005970499,0.0005452972,0.001920828,0.006590905,0.0004214722,0.001462357,0.002020271,0.001841423,0.07114543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327028,"about_ca_system_score_gemma":0.002805001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01142922,"about_ca_topic_score_gemma":0.02204605,"domain_scores_codex":[0.9973814,0.0004336229,0.000373925,0.0009917179,0.0005701967,0.0002490562],"domain_scores_gemma":[0.9976099,0.0006860566,0.0003465892,0.0005748862,0.0006809906,0.0001016816],"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":[0.0007983127,0.0001302764,0.006094906,0.01052208,0.0004338079,0.0001212962,0.00009038915,0.001391158,0.002989498,0.001636359,0.925664,0.05012795],"study_design_scores_gemma":[0.0002120144,0.00006285179,0.007828009,0.000677539,0.0001493883,0.0001410005,0.00006947439,0.0006360282,0.002968278,0.002057579,0.9851446,0.00005310665],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005994898,0.000782855,0.0008590911,0.00007268906,0.00005217622,0.00008509857,0.9946055,0.001062474,0.001880696],"genre_scores_gemma":[0.0008339855,0.0005110042,0.001724154,0.0000918283,0.00001199893,0.0002512296,0.9954855,0.0001143405,0.0009759921],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03667381,"threshold_uncertainty_score":0.1226861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01463492362922162,"score_gpt":0.3840467796609045,"score_spread":0.3694118560316829,"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."}}