{"id":"W4236373056","doi":"10.1515/iupac.88.0179","title":"External Calibration","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Calibration; Sample (material); Scale (ratio); Throughput; Process engineering; Data mining; Chromatography; Engineering; Chemistry; Mathematics; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005624033,0.002744207,0.001744254,0.004759647,0.001091285,0.003160884,0.004115312,0.00202096,0.09354756],"category_scores_gemma":[0.02679277,0.0007597057,0.001976347,0.008781047,0.0006499977,0.002669647,0.003017269,0.002355249,0.1396573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00153747,"about_ca_system_score_gemma":0.002876239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00845249,"about_ca_topic_score_gemma":0.01300392,"domain_scores_codex":[0.9929556,0.001365958,0.0009805644,0.002437161,0.001862337,0.0003984234],"domain_scores_gemma":[0.9874993,0.003345348,0.001191244,0.00335964,0.004383368,0.0002211671],"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":[0.0004476207,0.0001028272,0.007552941,0.00379048,0.0002866618,0.00006085749,0.0000779654,0.00182071,0.001375568,0.003008723,0.8965039,0.08497165],"study_design_scores_gemma":[0.0001303057,0.00003998371,0.006368404,0.0006320248,0.00007513351,0.00007618695,0.00006011259,0.0007639333,0.001570736,0.003364761,0.9868791,0.00003942096],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001267892,0.0009725793,0.005825403,0.0002154637,0.0002196612,0.0002131794,0.9781909,0.003001452,0.01009336],"genre_scores_gemma":[0.002939009,0.0005411027,0.006963052,0.0003160121,0.00004456929,0.0006464355,0.9826463,0.0006038981,0.005299508],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09354756,"threshold_uncertainty_score":0.3129478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0292063224048891,"score_gpt":0.4410391103808254,"score_spread":0.4118327879759364,"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."}}