{"id":"W4236995488","doi":"10.1515/iupac.88.0322","title":"Membrane Extraction","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; Sample (material); Sample preparation; Process engineering; Scale (ratio); Chromatography; Chemistry; Engineering; Physics","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":[0.002844803,0.002650263,0.002344264,0.004937245,0.001202713,0.002944104,0.00316877,0.002260687,0.06281395],"category_scores_gemma":[0.01212551,0.0007239342,0.002126744,0.007897247,0.0004586781,0.002009052,0.002542414,0.002326658,0.09112274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001850247,"about_ca_system_score_gemma":0.004882085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01078967,"about_ca_topic_score_gemma":0.0201136,"domain_scores_codex":[0.9966355,0.0006085223,0.0005546971,0.001149956,0.0007231627,0.0003281248],"domain_scores_gemma":[0.9955131,0.001370988,0.0006788429,0.0009142282,0.001321526,0.0002012482],"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":[0.0005380416,0.00005903658,0.003114675,0.01130095,0.0002889241,0.00008504137,0.00005697561,0.0006886086,0.001415458,0.001995349,0.9556848,0.02477225],"study_design_scores_gemma":[0.0002509942,0.00003187839,0.003575797,0.001201723,0.0001209917,0.00008002641,0.00005055956,0.0002474203,0.001055287,0.001947815,0.9914029,0.00003458578],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001902873,0.0004401428,0.0004255864,0.00007742825,0.0000336639,0.00006012397,0.9971688,0.0004396254,0.001164387],"genre_scores_gemma":[0.0003829575,0.0004292626,0.001164578,0.0001070004,0.000009499518,0.0002482438,0.9967918,0.00008785001,0.000778814],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06281395,"threshold_uncertainty_score":0.2101336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03188467874456576,"score_gpt":0.4616889078881239,"score_spread":0.4298042291435581,"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."}}