High sensitive analysis of steroids in doping control using gas chromatography/time‐of‐flight mass‐spectrometry
Bibliographic record
Abstract
The method of high sensitive gas chromatographic/time-of-flight mass-spectrometric (GC/TOF-MS) analysis of steroids was developed. Low-resolution TOF-MS instrument (with fast spectral acquisition rate) was used. This method is based on the formation of the silyl derivatives of steroids; exchange of the reagent mixture (pyridine and N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA)) for tert-butylmethylether; offline large sample volume injection of this solution based on sorption concentration of the respective derivatives from the vapour-gas mixture flow formed from the solution and inert gas flows; and entire analytes solvent-free concentrate transfer into the injector of the gas chromatograph. Detection limits for 100 µl sample solution volume were 0.5-2 pg/µl (depending on the component). Application of TOF-MS model 'TruTOF' (Leco, St Joseph, MO, USA) coupled with gas chromatograph and ChromaTOF software (Leco, St Joseph, MO, USA) allowed extraction of the full mass spectra and resolving coeluted peaks. Due to use of the proposed method (10 µl sample aliquot) and GC/TOF-MS, two times more steroid-like compounds were registered in the urine extract in comparison with the injection of 1 µl of the same sample solution.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".