Causes and Factors of Cryogenic Extraction Biases on Isotopes of Xylem Water
Bibliographic record
Abstract
Abstract Cryogenic vacuum extraction (CVE) has been considered as the standard technique for the analysis of plant water stable isotopes in ecohydrological research. Recent studies reported that CVE can introduce significant bias in stable isotope analyses, yet the causes and influencing factors of the CVE‐induced deuterium offsets remain poorly understood. Here, we performed rehydration experiments on plant samples from two species and three organs with two distinct‐isotopic spiking waters. Centrifugation and high‐pressure mechanical squeezing were used to separate sap water and tissue water for stable isotope analyses. Plant waters extracted by CVE differed significantly from reference waters in δ 2 H, but not in δ 18 O. The δ 2 H bias was linearly correlated to the xylem water content, and this relationship is affected significantly by plant organs/species and the isotopic signature of the spiking water. Moreover, the δ 2 H bias induced by CVE (−8.52 ± 0.90‰) was significantly greater than the δ 2 H difference between the tissue and sap waters (−3.33 ± 0.76‰) for apple stems possessing similar water contents. Thus, hydrogen‐exchange between plant organics and water, and isotopic heterogeneity within plants both contribute to the negative δ 2 H bias, but the former is the dominant cause. The multiple factors governing the CVE‐induced δ 2 H bias, make it difficult to establish a unified bias correction equation. Our results question the usefulness of cryogenic extraction as a standard for plant water extraction.
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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 | 0.001 | 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) | 0.007 | 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 teacher head, 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".