Beyond Deuterium: <scp> δ <sup>18</sup> O </scp> Offsets From Cryogenic Vacuum Extraction Bias Water Source Apportionment in <scp> <i>Tamarix chinensis</i> </scp>
Bibliographic record
Abstract
ABSTRACT Cryogenic vacuum extraction (CVE) is widely used to extract water from plant stems for analysis of stable water isotopes, yet it is known to induce significant δ 2 H offsets. Whether δ 18 O is similarly biased has remained uncertain, especially in halophytes that often depend solely on δ 18 O as a tracer. Here, we conducted controlled rehydration experiments on Tamarix chinensis stems with two isotopically distinct spiking waters (groundwater and tap water) to evaluate the isotopic offsets (Δδ) in the CVE‐extracted stem water relative to the spiking water. The CVE‐extracted water showed significant δ 18 O offset (Δδ 18 O), in addition to δ 2 H offset (Δδ 2 H). For the groundwater treatment, the Δδ 2 H and Δδ 18 O averaged −10.88‰ and −0.62‰, respectively, while in the tap water treatment Δδ 2 H and Δδ 18 O averaged −10.61‰ and −0.80‰. Δδ 18 O and Δδ 2 H were positively correlated and both became less negative with increasing stem relative water content, enabling an offset‐correction approach. Ignoring the δ 18 O offset led to approximately 9% underestimation of shallow soil water contribution and a corresponding overestimation of groundwater uptake in a mixing model. These results underscore the need to correct for CVE‐induced δ 18 O biases in plant water source studies, especially for halophytes. The linear relationship between isotopic offsets and stem relative water content offers a promising basis for correcting these biases, improving the accuracy of plant water source apportionment and our understanding of plant water use strategies.
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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 | 0.000 |
| 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".