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Record W2130417600 · doi:10.1139/x99-205

Leaf water relations of competitive <i>Fagus sylvatica</i> and <i>Quercus petraea</i> trees during 4 years differing in soil drought

2000· article· en· W2130417600 on OpenAlexvenueno aff
Katharina Backes, Christoph Leuschner

Bibliographic record

VenueCanadian Journal of Forest Research · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersNorthwestern University
KeywordsFagus sylvaticaQuercus petraeaTurgor pressureFagaceaeTemperate climateBeechSoil waterPhotosynthesisBotanyWater potentialTemperate rainforestBiologyTemperate forestWater contentAgronomyHorticultureEnvironmental scienceEcologyEcosystem

Abstract

fetched live from OpenAlex

Leaf water relations of competitive mature Fagus and Quercus trees were compared during four seasons with low, moderate, or high soil drought intensities in humid northwestern Germany. Leaf conductances (g l ) typically were higher by about 30% in Quercus than in Fagus sun leaves. Predawn leaf water potentials (Ψ pd ) and osmotic potentials (Π o , Π p ) were remarkably similar for the two species. Fagus had significantly lower leaf tissue elasticities (ε max ) than Quercus in dry but not in wet summers. Interannual variabilities in water status parameters were large for g l and seasonal bulk leaf turgor (P) minima; moderate for ε max and daily water potential (Ψ) minima; and small for Π o , Π p , and the relative leaf water content at zero turgor. Fagus regulated water loss conservatively with only limited reductions in P and Ψ in wet or moderately dry seasons but large decreases in Ψ pd ,g l , photosynthesis, and growth during occasional severe droughts. Quercus displayed patterns of a stress-tolerating species (lower ε max , a less drought-sensitive stomatal regulation, and apparently significant drought-induced osmotic adjustment). In temperate humid environments, tree water relations should be studied for at least three or four seasons to account for large interannual variabilities in water status parameters.

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 score0.237
Threshold uncertainty score0.996

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)0.0010.000

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 head0.010
GPT teacher head0.221
Teacher spread0.211 · 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

Citations98
Published2000
Admission routes1
Has abstractyes

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