{"id":"W4240059842","doi":"10.1515/iupac.88.0257","title":"QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) Extraction","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Nuclear Physics and Applications","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Quechers; Computer science; Extraction (chemistry); Process engineering; Sample preparation; Scale (ratio); Data extraction; Sample (material); Biochemical engineering; Chromatography; Engineering; Chemistry; MEDLINE; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002922977,0.0004084987,0.0005096902,0.00007928768,0.0004799418,0.0002293496,0.0003230646,0.000180371,0.001034424],"category_scores_gemma":[0.00002741328,0.0003980478,0.0001668704,0.00007965862,0.0001690266,0.0001648844,0.0001528782,0.0007276593,0.0000110368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001258384,"about_ca_system_score_gemma":0.0002601532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001187212,"about_ca_topic_score_gemma":0.0001321017,"domain_scores_codex":[0.9983941,0.00006313629,0.000278508,0.0005470584,0.0003977257,0.000319518],"domain_scores_gemma":[0.9982098,0.00008663029,0.0004161221,0.0008985481,0.0002127431,0.0001761858],"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":[0.00004073128,0.0002795711,0.00006309969,0.00005621911,0.0001560044,0.000002121094,0.0000128966,0.000002209754,0.0001095791,0.0004251461,0.9606849,0.03816751],"study_design_scores_gemma":[0.0006811299,0.00007282744,0.0009433773,0.0001243694,0.0001688417,0.000001427095,0.00003839197,0.00002938134,0.00006678106,0.002188441,0.9952835,0.0004015295],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001831169,0.0001268697,0.0004339445,0.0002328539,0.0002582817,0.0006052983,0.9955682,0.00003214447,0.0009111956],"genre_scores_gemma":[0.006875866,0.00008245361,0.00009878774,0.00004280529,0.00149873,0.0000884883,0.9908025,0.00005185399,0.0004585269],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03776598,"threshold_uncertainty_score":0.9998788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009874555225320597,"score_gpt":0.4069275569591258,"score_spread":0.3970530017338052,"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."}}