{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008083796,0.000662386,0.0008897086,0.0001000014,0.0002552364,0.0001729849,0.001058804,0.001109962,0.01965562],"category_scores_gemma":[0.002279393,0.0006345224,0.0002728395,0.00007280165,0.0002466032,0.0001227431,0.0003281624,0.001465888,0.000005975285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001024294,"about_ca_system_score_gemma":0.0009709106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008048159,"about_ca_topic_score_gemma":0.00008166285,"domain_scores_codex":[0.9965093,0.00003511081,0.0006867824,0.0008175579,0.001430191,0.0005210525],"domain_scores_gemma":[0.9964579,0.0001663536,0.0006779608,0.001930276,0.0004884244,0.0002790767],"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":[0.0001053967,0.0002103785,0.00000237658,0.000933922,0.0001673875,0.0001954181,0.0000031517,1.138206e-7,0.004132014,9.926943e-7,0.9858215,0.008427318],"study_design_scores_gemma":[0.0002591585,0.00001797885,0.000001673023,0.0004833028,0.0002285199,0.00005902732,0.0000179887,0.000006073078,0.0801973,0.00006011749,0.9180295,0.0006392949],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001207048,0.0001724306,0.001077939,0.0002767173,0.0002199328,0.0000979488,0.9943105,0.0002430796,0.003589402],"genre_scores_gemma":[0.000005784023,0.0009755904,0.007519136,0.00008983359,0.001337869,0.00003460239,0.9844944,0.00006635718,0.005476463],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07606529,"threshold_uncertainty_score":0.9996106,"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."}}