{"id":"W4229874104","doi":"10.1515/iupac.88.0378","title":"Membrane Extraction Techniques","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Sample (material); Extraction (chemistry); Computer science; Sample preparation; Perspective (graphical); Scale (ratio); Biochemical engineering; Data science; Data mining; Artificial intelligence; Chromatography; Engineering; Chemistry; Geography","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":[0.002737271,0.00195918,0.001867994,0.00448361,0.0009106546,0.002265722,0.002534991,0.001583114,0.036088],"category_scores_gemma":[0.01024405,0.000548674,0.001933872,0.007498058,0.0004712847,0.001505805,0.001958529,0.001753641,0.05364839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00151574,"about_ca_system_score_gemma":0.00341287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007082706,"about_ca_topic_score_gemma":0.01240673,"domain_scores_codex":[0.9962198,0.0006082406,0.0007890218,0.001182344,0.000931436,0.000269202],"domain_scores_gemma":[0.9956627,0.001468714,0.0007964469,0.0008498718,0.001071599,0.0001507211],"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":[0.001032258,0.0001289113,0.006648072,0.01919658,0.0004972946,0.0001556885,0.0001046221,0.001469586,0.003068198,0.002674492,0.8877146,0.07730971],"study_design_scores_gemma":[0.0001443127,0.00004495044,0.005662967,0.0009876812,0.0001143506,0.0001347563,0.00005037582,0.0002761715,0.001832886,0.001802315,0.9889123,0.00003692751],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007865482,0.001383877,0.001459977,0.0001463127,0.00005290671,0.0001647421,0.9927752,0.0008961934,0.002334326],"genre_scores_gemma":[0.001235028,0.001221608,0.00354607,0.0001413901,0.00001736359,0.0005039744,0.9920354,0.0001401058,0.001159066],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.036088,"threshold_uncertainty_score":0.1207263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02913784889487388,"score_gpt":0.4650704397944539,"score_spread":0.43593259089958,"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."}}