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 H2Oliquid–H2Ovapour 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 δ2H and δ18O 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 δ2H and δ18O than CVD‐LS, with root mean square error (RMSE) of 8.06‰ for δ2H and 0.98‰ for δ18O. Calibration with soil texture reduced RMSE to 3.53‰ and 0.72‰ for δ2H and δ18O, respectively. Further, calibration with both soil texture and water content decreased RMSE to 3.10‰ for δ2H and 0.73‰ for δ18O. 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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".