Improved accuracy and precision of water stable isotope measurements using the direct vapour equilibration method
Bibliographic record
Abstract
Rationale A method to measure the δ 2 H and δ 18 O composition of pore water in soil samples using direct vapour equilibration and laser spectrometry was first described in 2008, and was rapidly adopted. Here, we describe an improved setup to measure pore water δ 2 H and δ 18 O values through direct vapour equilibration with a laser spectrometer, combining a liquid and a vapour mode for water isotope analyses, and resulting in improved accuracy. Methods We first tested new gas sampling bags as part of the equilibration protocol. Then, to assess measurement accuracy, vapour samples from equilibrated liquid waters of known isotope composition were measured in the liquid mode of the analyser using the new setup as well as the manufacturer's vapour mode. Various modes of preparing liquid water standards, namely equilibration, nebulisation, and vapourisation, were tested to determine the best calibration in terms of accuracy. Finally, the proposed modified liquid setup was validated by analysing water vapour equilibrated from soil pore water of a known composition. Results The δ 2 H and δ 18 O measurements were found to be more accurate by the modified liquid mode than by the factory‐setup vapour mode. The strong and non‐linear dependence of measured δ 2 H and δ 18 O values on H 2 O concentration in vapour mode, especially at concentrations equal to the vapour pressure saturation typically found in laboratories, is problematic for corrections. Regarding calibration and standards, the use of two equilibrated liquid water standards was found to best calibrate measurements in the modified liquid setup. Finally, the modified liquid mode setup and its calibration, as described here, were shown to be appropriate for soil pore water analysis. Conclusions The proposed modified setup results in more precise δ 2 H and δ 18 O soil pore water values than the usual protocols. An average standard deviation of 0.04‰ for δ 18 O values and 0.3‰ for δ 2 H values, based on 228 soil sample analyses, was obtained.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".