{"id":"W4242702895","doi":"10.1515/iupac.88.0366","title":"Introduction: Perspective on Sample Preparation","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Statistics Education and Methodologies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Sample (material); Perspective (graphical); Sample preparation; Computer science; Extraction (chemistry); Scale (ratio); Data science; Biochemical engineering; Process engineering; Data mining; Chromatography; Artificial intelligence; Engineering; Chemistry","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":[0.01206572,0.001336013,0.001365582,0.003703468,0.0009559751,0.00573151,0.002877232,0.002450328,0.04256358],"category_scores_gemma":[0.05767758,0.000688974,0.001588467,0.008507429,0.001144865,0.003925299,0.003314824,0.003727318,0.05067549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002335864,"about_ca_system_score_gemma":0.003917837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004684416,"about_ca_topic_score_gemma":0.006514069,"domain_scores_codex":[0.9885556,0.004527614,0.001532094,0.002250165,0.00271834,0.0004162454],"domain_scores_gemma":[0.9687187,0.01569798,0.002547379,0.006225945,0.005904948,0.0009050105],"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":[0.0001749083,0.00002796586,0.002273225,0.003035717,0.00008544651,0.00002695423,0.00007908692,0.0006650102,0.0004756604,0.009463142,0.9208721,0.06282075],"study_design_scores_gemma":[0.0000237965,0.00001515771,0.001636603,0.0007653279,0.0000140507,0.00006304701,0.00003985392,0.0001512653,0.000274792,0.005991797,0.9910086,0.00001585305],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001641006,0.01948338,0.02575341,0.02325805,0.003090805,0.0005201326,0.900388,0.002092059,0.02377315],"genre_scores_gemma":[0.006881218,0.01575973,0.04114862,0.01630626,0.002411339,0.002343293,0.9042247,0.001341646,0.009583237],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04256358,"threshold_uncertainty_score":0.1423894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1235352722829374,"score_gpt":0.5822321021463256,"score_spread":0.4586968298633882,"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."}}