Improved online <i>δ</i> <sup>18</sup> O measurements of nitrogen‐ and sulfur‐bearing organic materials and a proposed analytical protocol
Bibliographic record
Abstract
It is well known that N(2) in the ion source of a mass spectrometer interferes with the CO background during the δ(18)O measurement of carbon monoxide. A similar problem arises with the high-temperature conversion (HTC) analysis of nitrogenous O-bearing samples (e.g. nitrates and keratins) to CO for δ(18)O measurement, where the sample introduces a significant N(2) peak before the CO peak, making determination of accurate oxygen isotope ratios difficult. Although using a gas chromatography (GC) column longer than that commonly provided by manufacturers (0.6 m) can improve the efficiency of separation of CO and N(2) and using a valve to divert nitrogen and prevent it from entering the ion source of a mass spectrometer improved measurement results, biased δ(18)O values could still be obtained. A careful evaluation of the performance of the GC separation column was carried out. With optimal GC columns, the δ(18)O reproducibility of human hair keratins and other keratin materials was better than ± 0.15 ‰ (n=5; for the internal analytical reproducibility), and better than ± 0.10 ‰ (n=4; for the external analytical reproducibility).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".