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Record W4412896510 · doi:10.1093/treephys/tpaf096

White spruce (<i>Picea glauca</i>) population differences in needle anatomy, foliar water uptake and aquaporin expression indicate trade-offs between hydraulic safety and productivity

2025· article· en· W4412896510 on OpenAlexaff
Killian Fleurial, Jaime Sebastian‐Azcona, Andreas Hamann, Janusz J. Zwiazek

Bibliographic record

VenueTree Physiology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiologyDewDrought toleranceAdaptation (eye)AquaporinLocal adaptationPopulationBotanyCanopyAgronomyEcologyPhysiologyGeography

Abstract

fetched live from OpenAlex

White spruce is a leading species across nearly the entirety of the North American boreal forest, occurs under a wide range of climate conditions and has been reported to take up water through its needles. As such, the species represents a good model organism in which to research adaptation to climatic factors through structural and physiological mechanisms. We used branch samples obtained from a 40-year-old range-wide provenance experiment to relate the climate of origin to needle anatomy, foliar water uptake and aquaporin expression under simulated drought conditions. Provenances with cold and dry source climates generally had thinner needle hypodermis layers and Casparian strips, and lost more water during dehydration. However, foliar water uptake, which involved the regulation of aquaporin water channel gene expression, was also highest in these provenances. We propose that the absence of foliar anatomical traits that would typically be associated with drought adaptation represents a previously undocumented drought adaptation strategy: a thin hypodermis and Casparian strip with aquaporin-mediated water uptake enables distinct spruce populations to leverage foliar wetting events such as snowmelt, dew or light rain, when water uptake in roots is seasonally restricted by low soil temperatures. However, this strategy is vulnerable to severe or prolonged drought events.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.468

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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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 designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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