Carbon isotope discrimination by <i>Picea glauca</i> and <i>Populus tremuloides</i> is related to the topographic depth to water index and rainfall
Bibliographic record
Abstract
Carbon isotope ratio (δ13C) has been used as an indicator of water stress because plants discriminate less against 13C when under stress. The depth-to-water (DTW) topographic index provides an estimation of soil moisture based on topographic position and other characteristics of a site. To evaluate whether DTW and carbon isotope discrimination were related and to determine if these relationships are influenced by climate, we sampled three time periods, which differed in the amount of annual precipitation (MAP), from tree cores collected from 42 trembling aspen and 43 white spruce trees growing along DTW gradients at two locations in central Alberta, Canada. Increasing MAP led to lower δ13C, indicating less drought stress as water availability increases, while δ13C increased with DTW up to a threshold value, after which the relationship levelled off, suggesting that higher DTW values represent stress-inducing soil conditions. DTW and MAP were then combined into models (aspen, R2 = 0.72; spruce, R2 = 0.44) that could be used to delineate drought-prone areas during periods of low MAP. Tree height and diameter were also related to DTW, suggesting a functional relationship between an index capturing soil properties and tree size. Our results demonstrate the potential to use the DTW index as a measure of site conditions and to predict stand-level responses.
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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 | 0.001 | 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".