{"id":"W4237487035","doi":"10.1515/iupac.88.0323","title":"Membrane Extraction With Sorbent Interface (MESI)","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Membrane-based Ion Separation Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Sorbent; Extraction (chemistry); Computer science; Interface (matter); Sample preparation; Microwave; Process engineering; Throughput; Scale (ratio); Chromatography; Chemistry; Engineering; Physics; Parallel computing; Adsorption","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":[0.003058872,0.002995828,0.002343479,0.005236777,0.0009972659,0.002669285,0.003220647,0.001998636,0.02002259],"category_scores_gemma":[0.009571665,0.0006163492,0.002693244,0.007197395,0.0005218541,0.001588806,0.002911465,0.002011897,0.03736875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001456821,"about_ca_system_score_gemma":0.003667444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009801965,"about_ca_topic_score_gemma":0.01971458,"domain_scores_codex":[0.9964179,0.0006958813,0.0006842145,0.001160622,0.000725728,0.0003156532],"domain_scores_gemma":[0.996393,0.001140868,0.0007054633,0.0008190255,0.0007636538,0.000178019],"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":[0.0009902844,0.0001395074,0.008047841,0.02452395,0.0007315155,0.000144079,0.00009995643,0.001558016,0.002478147,0.001583223,0.9150642,0.04463942],"study_design_scores_gemma":[0.0003736579,0.00009501809,0.01123102,0.001695892,0.0003192403,0.0001472804,0.00006693396,0.000764738,0.002377844,0.001824407,0.981038,0.00006595478],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004747901,0.0008664512,0.0005123246,0.00008669298,0.00004555491,0.00007627978,0.9964067,0.0006825121,0.0008485997],"genre_scores_gemma":[0.0007032406,0.0004768156,0.001548058,0.0000866765,0.0000105805,0.0003035674,0.9962734,0.0000805068,0.0005171499],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02002259,"threshold_uncertainty_score":0.06698227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01551029982565673,"score_gpt":0.4106310447839145,"score_spread":0.3951207449582578,"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."}}