Extension of the triphasic water potential curve: Accounting for air vapor pressure deficit under soil water stress
Bibliographic record
Abstract
For woody plants subjected to soil dehydration, physiological thresholds of drought-induced stomatal closure (i.e. minimum stomatal conductance, gs-min) and turgor loss point (TLP) can be derived from the triphasic relationship of stem water potential (Ψ) at midday and predawn, i.e. the "Ψ-curve". In this study, we provide an extension of the Ψ-curve approach that accounts for vapor pressure deficit (VPD). Experimental data were collected in a greenhouse for potted hazelnut (Corylus avellana) trees (varieties "Jefferson" and "Yamhill")-a species known for its VPD-sensitivity. Consistent with the original Ψ-curve, the "VPD-adjusted" Ψ-curve exhibited a triphasic shape. Predicted thresholds of Θ1' and Θ2' were comparable to independent measures of gs-min (-0.86 "Jefferson"; -1.16 MPa "Yamhill") and TLP (-1.76 MPa "Jefferson"; -2.06 MPa "Yamhill"), respectively. In conclusion, the extended Ψ-curve approach allows the separatation of soil from atmospheric water stress when predicting physiological thresholds using stem water potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".