{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001141147,0.0003587971,0.0005759664,0.0002012662,0.0003190551,0.0001529254,0.0004028043,0.0002908272,0.004111952],"category_scores_gemma":[0.05885836,0.0003191358,0.0001213541,0.0000786263,0.0001747713,0.00006882886,0.00008269247,0.0005291436,0.00001024658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009279908,"about_ca_system_score_gemma":0.0008808015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001830123,"about_ca_topic_score_gemma":0.0006721845,"domain_scores_codex":[0.997746,0.0002593254,0.0003895077,0.0005907277,0.0007591625,0.0002552804],"domain_scores_gemma":[0.9950319,0.001640553,0.0006058004,0.001499543,0.001128846,0.0000933471],"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":[0.0002419284,0.0003092221,4.669275e-7,0.0001062544,0.00009729955,0.000004434165,0.0001599112,0.000002808946,6.702486e-7,0.009256205,0.9893301,0.0004906917],"study_design_scores_gemma":[0.000302286,0.0003041708,0.000009636366,0.00007334808,0.0001548492,0.000008942341,0.0004722731,0.000004549739,0.00002426679,0.1092008,0.8891666,0.0002782679],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001371305,0.00009189074,0.006236302,0.003192575,0.004636356,0.000417294,0.985109,0.00009662756,0.0002062288],"genre_scores_gemma":[0.000001851276,0.0002511552,0.01945307,0.0001246107,0.005192659,0.0000403521,0.9725366,0.00003263611,0.002367082],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1001635,"threshold_uncertainty_score":0.9999261,"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."}}