Improved Method for Determining the Stable-Hydrogen Isotopic Composition (δD) of Complex Organic Materials of Environmental Interest
Bibliographic record
Abstract
Despite new and increasing applications in environmental studies, determinations of the stable-hydrogen isotope (δD) composition of complex organic substrates are hampered by laborious preparation techniques and uncontrolled isotopic exchange between labile hydrogen in the sample and ambient water vapor. To date, there has been little agreement in the way laboratories prepare, measure, and correct for uncontrolled hydrogen isotopic exchange in complex organic samples, resulting in incomparable δD results. Previously, nitration and preparative equilibration procedures aimed at controlling exchangeable hydrogen were designed for specific sample types (e.g. cellulose, chitin) but required extensive processing of individual samples. Here we describe a static, high-temperature preparative equilibration technique that provides increased sample throughput and δD measurements that are not compromised by uncontrolled isotopic exchange. By adopting this approach, it will be possible to compare complex organic δD measurements among laboratories. The current inconsistency in organic δD measurements among laboratories seriously hampers comparison of results among studies. As pyrolitic continuous-flow δD analyses become available, the preparative equilibration method described can be adapted to suit the smaller size requirements.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".