{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004635174,0.00065212,0.0007388588,0.0003380006,0.0001638995,0.000238235,0.0006258789,0.0005047052,0.002353335],"category_scores_gemma":[0.0001246029,0.000580535,0.0001254976,0.000123118,0.0001138747,0.0003428998,0.00006475551,0.0009846971,0.00001340799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005646719,"about_ca_system_score_gemma":0.0003199874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008669386,"about_ca_topic_score_gemma":0.0008936212,"domain_scores_codex":[0.9973865,0.00005681998,0.0005623296,0.0004988222,0.00108315,0.0004123532],"domain_scores_gemma":[0.9975544,0.00006267469,0.0003124745,0.001527304,0.0003548518,0.0001883432],"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":[0.000166964,0.00008048207,6.198892e-7,0.0005069926,0.0001313645,0.00005955049,0.00001266351,0.00143023,0.0007109467,0.000001929888,0.9954647,0.001433547],"study_design_scores_gemma":[0.000624404,0.0002226879,0.000005523065,0.0005901169,0.0001288344,0.00005289708,0.00001533692,0.0005442223,0.02036152,0.000009584974,0.9768067,0.0006382002],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002715363,0.0005183105,0.01220468,0.0001236943,0.0008220954,0.0005542195,0.9843152,0.0009073298,0.0002829806],"genre_scores_gemma":[0.0007646009,0.001505024,0.0006206657,0.0000543409,0.0004746087,0.00006624027,0.9959284,0.0001233503,0.0004627669],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01965057,"threshold_uncertainty_score":0.9996646,"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."}}