On the use of leaf water to determine plant water source: A proof of concept
Bibliographic record
Abstract
Abstract Source water apportionment studies using the dual isotopes of oxygen and hydrogen have revolutionized our understanding of ecohydrology. But despite these developments—mostly over the past decade—many technical problems still exist in terms of linking xylem water to its soil water and groundwater sources. This is mainly due to sampling issues and possible fractionation of xylem water. Here we explore whether or not leaf water alone can be used to quantify the blend of rainfall event inputs from which the leaf water originates. Leaf water has historically been avoided in plant water uptake studies due to the extreme fractionation processes at the leaf surface. In our proof of concept work we embrace those processes and use the well‐known Craig and Gordon model to map leaf water back to its individual precipitation event water sources. We also employ a Bayesian uncertainty estimation approach to quantify source apportionment uncertainties. We show this using a controlled, vegetated lysimeter experiment where we were able to use leaf water to correctly identify the mean seasonal rainfall that was taken up by the plant, with an uncertainty typically within ±1‰ for δ18O. While not appropriate for all source water studies, this work shows that leaf water isotope composition may provide a new, relatively un‐intrusive method for addressing questions about the plant water source.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".