Direct comparison of four methods to construct xylem vulnerability curves: Differences among techniques are linked to vessel network characteristics
Bibliographic record
Abstract
During periods of dehydration, water transport through xylem conduits can become blocked by embolism formation. Xylem embolism compromises water supply to leaves and may lead to losses in productivity or plant death. Vulnerability curves (VCs) characterize plant losses in conductivity as xylem pressures decrease. VCs are widely used to characterize and predict plant water use at different levels of water availability. Several methodologies for constructing VCs exist and sometimes produce different results for the same plant material. We directly compared four VC construction methods on stems of black cottonwood (Populus trichocarpa), a model tree species: dehydration, centrifuge, X-ray-computed microtomography (microCT), and optical. MicroCT VC was the most resistant, dehydration and centrifuge VCs were intermediate, and optical VC was the most vulnerable. Differences among VCs were not associated with how cavitation was induced but were related to how losses in conductivity were evaluated: measured hydraulically (dehydration and centrifuge) versus evaluated from visual information (microCT and optical). Understanding how and why methods differ in estimating vulnerability to xylem embolism is important for advancing knowledge in plant ecophysiology, interpreting literature data, and using accurate VCs in water flux models for predicting plant responses to drought.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".