Plant source water apportionment using stable isotopes: A comparison of simple linear, two‐compartment mixing model approaches
Bibliographic record
Abstract
Abstract Plant source water identification using stable isotopes is now a common practice in ecohydrological process investigations. Notwithstanding, little critical evaluation of the approaches for source apportionment have been conducted. Here, we present a critical evaluation of the main methods used for source apportionment between vadose and saturated zone water: simple mass balance and Bayesian mixing models. We leverage new isotope stem water samples from a diverse set of tree species in a strikingly uniform terrain and soil conditions at the Christchurch Botanic Garden, New Zealand. Our results show that using δ2H alone in a simple, two‐source mass balance approach leads to erroneous results, particularly an apparent overestimation of groundwater contribution to xylem. Alternatively, using both δ2H and δ18O in a Bayesian inference framework improves the source water estimates and is more useful than the simple mass balance approach, particularly when soil and groundwater contributions are relatively disproportionate. We suggest that plant source water quantification methods should take into consideration the possible effects of 2H/1H fractionation. The Bayesian inference approach used here may be less sensitive to 2H/1H fractionation effects than simple mass balance methods.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".