Influence of soil organic matter on stable water isotope analysis of soil pore water using the H2O(liquid)–H2O(vapor) vapor equilibration method
Bibliographic record
Abstract
• δ 18 O and δ 2 H were analyzed using the direct liquid–vapor equilibration method. • Influence of soil texture, volumetric water content and organic matter were shown. • Mineral-water interactions significantly influenced δ 18 O and δ 2 H measurements. • A regression using volumetric water content and organic matter is presented. The direct liquid–vapor equilibration (DLVE) method is commonly used to analyze the stable isotope composition of soil pore water ( δ 18 O, δ 2 H) by laser spectroscopy. The influence of soil texture, soil volumetric water content (VWC), and the presence of organic matter impacting reliable results is not yet fully understood. We conducted two different experiments: in the first experiment, natural silty topsoil with 4 % organic matter, chemically washed soil with 1 % organic matter, and two non-organic sediments (sand and kaolinite) were used to study the impact of soil organic matter on the DLVE method for a gradient of volumetric porewater contents. In the second experiment, different concentrations of humic acids were mixed with sandy sediment and porewater of known isotopic composition to investigate whether organic matter alone or its interactions with the sediment impacted the DLVE measurements. Results showed an influence of VWC on measured vapor isotope compositions, especially during drier conditions, with an unexpected lower accuracy (larger bias from the known isotope ratio) for sandy sediment compared to kaolinite when VWC was below 0.15 cm 3 cm −3 . Besides VWC and soil texture, organic matter additionally altered the expected vapor isotope composition, as natural silty topsoil with 4 % organic matter showed lower accuracy at all volumetric water contents than the silty soil with only 1 % organic matter. Results of the second experiment showed that humic acids were significantly (p < 0.05) influencing DLVE outcomes regardless of the humic acid concentrations for the soil–water mixture samples, with a threshold behavior of markedly decreased accuracy above 2 % humic acid concentration for both δ 18 O and δ 2 H. As pure mixtures of water and humic acid generally had higher accuracy, the interaction of humic acid with the soil might be a factor. A multivariate regression analysis was carried out using volumetric water content and soil organic matter as independent variables to correct for biases. While for oxygen isotopes the results could be improved, hydrogen isotopes still showed low accuracy especially under dry conditions and larger percentages of humic acid. Our findings indicate that in addition to soil texture and volumetric water content, the interaction between the soil matrix and organic matter probably affects the DLVE method measurements, particularly at concentrations exceeding 0.1 g of humic acid per ml of water. In certain instances, this interaction may be corrected, thus addressing existing methodological gaps.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".