Calibration method affects the measured δ<sup>2</sup>H and δ<sup>18</sup>O in soil water by direct H<sub>2</sub>O<sub>liquid</sub>–H<sub>2</sub>O<sub>vapour</sub> equilibration with laser spectroscopy
Bibliographic record
Abstract
Abstract The direct H 2 O liquid –H 2 O vapour equilibration method utilizing laser spectroscopy (DVE‐LS) is a way to measure soil pore water stable isotopes. Various equilibration times and calibration methods have been used in DVE‐LS. Yet little is known about their effects on the accuracy of the obtained isotope values. The objective of this study was to evaluate how equilibration time and calibration methods affect the accuracy of DVE‐LS. We did both spiking and field soil experiments. For the spiking experiment, we applied DVE‐LS to four soils of different textures, each of which was subjected to five water contents and six equilibration times. For the field soil experiment, we applied three calibration methods for DVE‐LS to two field soil profiles, and the results were compared with cryogenic vacuum distillation (CVD)‐LS. Results showed that DVE‐LS demonstrated higher δ 2 H and δ 18 O as equilibration time increased, but 12 to 24 hr could be used as optimal equilibration time. For field soil samples, DVE‐LS with liquid waters as standards led to significantly higher δ 2 H and δ 18 O than CVD‐LS, with root mean square error (RMSE) of 8.06‰ for δ 2 H and 0.98‰ for δ 18 O. Calibration with soil texture reduced RMSE to 3.53‰ and 0.72‰ for δ 2 H and δ 18 O, respectively. Further, calibration with both soil texture and water content decreased RMSE to 3.10‰ for δ 2 H and 0.73‰ for δ 18 O. Our findings conclude that the calibration method applied may affect the measured soil water isotope values from DVE‐LS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".