{"id":"W4245291510","doi":"10.1515/iupac.88.0192","title":"Matrix","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Matrix (chemical analysis); Sample (material); Sample preparation; Process engineering; Chromatography; Chemistry; Engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002645066,0.002532102,0.001732072,0.003849841,0.001177462,0.003912688,0.003099021,0.001828136,0.131375],"category_scores_gemma":[0.01764611,0.0007057579,0.002147712,0.006748949,0.0004272439,0.002427374,0.002865564,0.002248445,0.1528095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001665367,"about_ca_system_score_gemma":0.003780464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01224911,"about_ca_topic_score_gemma":0.0247303,"domain_scores_codex":[0.9963077,0.0006484102,0.0005886158,0.001319443,0.0008631459,0.0002727144],"domain_scores_gemma":[0.9930522,0.00222423,0.0008716556,0.001476764,0.002137998,0.000237164],"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":[0.0002750093,0.00003788968,0.002827534,0.004035288,0.0001661792,0.00003937423,0.00004220631,0.000470783,0.0005420923,0.001555139,0.955416,0.03459247],"study_design_scores_gemma":[0.0001442794,0.00002859119,0.003715885,0.0007309214,0.00008564774,0.00009364183,0.00005513536,0.0003883086,0.0006135582,0.003600664,0.9905154,0.00002801635],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002449689,0.0005600877,0.001013795,0.0001672253,0.0000739389,0.00008087961,0.9947405,0.0008461686,0.00227244],"genre_scores_gemma":[0.0008710579,0.0005067105,0.00291344,0.000196796,0.00002861851,0.0003244587,0.9931737,0.0001945979,0.001790621],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.868625,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02175883550371452,"score_gpt":0.4846080549096582,"score_spread":0.4628492194059437,"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."}}