Bias estimation in the certification of steroid reference materials for carbon isotope delta measurements via elemental analyser and gas chromatography‐combustion‐isotope ratio mass spectrometry
Bibliographic record
Abstract
RATIONALE: C) ‰. These materials have been designed to assist anti-doping laboratories in validating their calibration method or to be employed as calibrant for stable carbon isotope measurements of Boldenone, Boldenone Metabolite 1 and Formestane. These CRMs will allow for accurate and traceable analysis in compliance with World Anti-Doping Agency (WADA) Technical Document TD2021IRMS. METHODS: Certification was performed using an elemental analyser-isotope ratio mass spectrometry (EA-IRMS) primary reference method on the bulk carbon isotope ratios of nominally pure steroid starting materials. EA-IRMS analyses were carried out on a Flash EA Isolink CN coupled via a Conflo IV to a Delta V plus mass spectrometer. Confirmation analysis was performed by gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS) using a Trace 1310 GC coupled to a Delta V plus mass spectrometer via GC Isolink II. RESULTS: C) values of -30.38‰ (Boldenone), -29.71‰ (Boldenone Metabolite 1) and 30.71‰ (Formestane). Noting that the assumption of 100% purity in the starting materials has the potential to introduce bias, this was investigated using GC-C-IRMS analysis and theoretical modelling based on purity assessment data. CONCLUSIONS: Careful application of this theoretical model was shown to provide reasonable estimates of uncertainty while avoiding the introduction of errors associated with analyte-specific fractionation during GC-C-IRMS analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".