Influence of soil texture and degree of saturation on the equilibration time of water isotope in closed systems using direct H2O(liquid) - H2O(vapour) equilibration method
Bibliographic record
Abstract
The direct liquid-vapour equilibration (DLVE) method is a new method to measure the stable isotopes of oxygen and hydrogen in soil pore water. Advantages of DLVE are (a) minimum sample handling, (b) direct isotope measurement from the samples without the need of extracting the water, (c) comparatively low costs, (d) and high reliability. However, the impact of different water content and equilibration times on the isotope measurement of different soil types is not well understood yet. Therefore, this study focuses on advancing our knowledge of the effect of different soil types and soil water contents on the isotope measurement of the DLVE method. Three different types of soil (sand, silt and clay) representing sediment samples with different pore sizes were saturated using tap water with a known isotopic value in a water bath. Different degrees of saturation (100%, 80%, 60% and 40%) were established, placed in Ziploc bags and equilibrated for different time spans ranging from 1 hour up to 8 days at constant surrounding temperature (about 20oC). The isotope measurements were obtained using cavity ring down laser spectroscopy (CDRS) for each test samples. The time taken for the H2O(liquid)-H2O(vapour) equilibration for different soil textures and different water contents in Ziploc bags were determined. Results showed that sandy soil samples took shorter time to reach isotopic equilibrium with the headspace in the Ziploc bags compared to clayey soil which took comparatively longer for the same soil saturation level. Regardless of the soil type, 100% saturated soil samples took shorter time to reach liquid-water equilibration compared to low saturated soil samples. These findings could lead to protocols of soil sample measurements using DLVE regarding the influence of different soil textures and soil moisture contents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".