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Record W4404239556 · doi:10.1111/ppl.14619

Linking stomatal size and density to water use efficiency and leaf carbon isotope ratio in juvenile and mature trees

2024· article· en· W4404239556 on OpenAlexaff
Peter Petrík, Anja Petek, Laurent J. Lamarque, Roman M. Link, Pierre‐André Waite, Nadine K. Ruehr, Bernhard Schuldt, Vincent Maire

Bibliographic record

VenuePhysiologia Plantarum · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsUniversité du Québec à Trois-RivièresNatural Resources CanadaCanadian Forest Service
FundersWaldklimafonds
KeywordsStomatal densityJuvenileIsotopes of carbonIsotopeCarbon fibersEnvironmental scienceWater-use efficiencyChemistryBotanyBiologyEnvironmental chemistryEcologyMathematicsPhotosynthesisTotal organic carbonPhysics

Abstract

fetched live from OpenAlex

Abstract Water‐use efficiency (WUE) is affected by multiple leaf traits, including stomatal morphology. However, the impact of stomatal morphology on WUE across different ontogenetic stages of tree species is not well‐documented. Here, we investigated the relationship between stomatal morphology, intrinsic water‐use efficiency (iWUE) and leaf carbon isotope ratio (δ 13 C). We sampled 190 individuals, including juvenile and mature trees belonging to 18 temperate broadleaved tree species and 9 genera. We measured guard cell length (GCL), stomatal density (SD), specific leaf area (SLA), iWUE and bulk leaf δ 13 C as a proxy for long‐term WUE. Leaf δ 13 C correlated positively with iWUE across species in both juvenile and mature trees, while GCL showed a negative and SD a positive effect on iWUE and leaf δ 13 C. Within species, however, only GCL was significantly associated with iWUE and leaf δ 13 C. SLA had a minor negative influence on iWUE and leaf δ 13 C, but this effect was inconsistent between juvenile and mature trees. We conclude that GCL and SD can be considered functional morphological traits related to the iWUE and leaf δ 13 C of trees, highlighting their potential for rapid phenotyping approaches in ecological studies.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.349

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 head0.193
Teacher spread0.187 · 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

Citations22
Published2024
Admission routes1
Has abstractyes

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