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Record W2071715163 · doi:10.1139/b08-093

Relationships between leaf life span, leaf mass per area, and leaf nitrogen cause different altitudinal changes in leaf δ<sup>13</sup>C between deciduous and evergreen species

2008· article· en· W2071715163 on OpenAlexvenueno aff
Koichi Takahashi, Y. Miyajima

Bibliographic record

VenueBotany · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEvergreenDeciduousBiologyBotanyAltitude (triangle)

Abstract

fetched live from OpenAlex

We examined the variability in stable carbon isotope ratio (δ 13 C) in leaves of two deciduous broad-leaved species and two evergreen conifer species along an altitudinal gradient in central Japan. The δ 13 C of the two deciduous species decreased with altitude, except near the upper distribution limit. The two evergreen species, however, showed no clear altitudinal trends for δ 13 C. The δ 13 C of the two deciduous species was positively correlated with leaf mass per area (LMA), indicating that the altitudinal variation in δ 13 C was controlled by LMA. Leaf nitrogen per mass (as a proxy of assimilation capacity, N mass ) was negatively correlated with LMA for the two deciduous species, while it was not correlated with LMA for the two evergreen species. Leaf life span of the two deciduous species decreased with altitude, whereas that of the two evergreen species increased. Thus, the two deciduous species had shorter-lived thinner leaves with higher N mass at higher altitudes, and the two evergreen species had longer-lived leaves. These changes contribute to the positive carbon balance at higher altitudes. Therefore, the different changes in δ 13 C with altitude between the deciduous and evergreen species are ascribed to the different altitudinal changes in the leaf traits for carbon balance.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.042
GPT teacher head0.218
Teacher spread0.176 · 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.

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

Citations37
Published2008
Admission routes1
Has abstractyes

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