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Record W4412793363 · doi:10.1093/plphys/kiaf337

Extension of the triphasic water potential curve: Accounting for air vapor pressure deficit under soil water stress

2025· article· en· W4412793363 on OpenAlexafffund
Steven T. Bristow, Thorsten Knipfer

Bibliographic record

VenuePLANT PHYSIOLOGY · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of California, DavisCanada Foundation for Innovation
KeywordsVapour Pressure DeficitStomatal conductanceTurgor pressureWater potentialWater stressSoil waterDehydrationEnvironmental scienceHorticultureWater contentChemistryBotanySoil scienceTranspirationBiologyGeologyPhotosynthesis

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.201
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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