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
Bibliographic record
Abstract
We examined the variability in stable carbon isotope ratio (δ13C) in leaves of two deciduous broad-leaved species and two evergreen conifer species along an altitudinal gradient in central Japan. The δ13C 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 δ13C. The δ13C of the two deciduous species was positively correlated with leaf mass per area (LMA), indicating that the altitudinal variation in δ13C was controlled by LMA. Leaf nitrogen per mass (as a proxy of assimilation capacity, Nmass) 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 Nmass 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 δ13C with altitude between the deciduous and evergreen species are ascribed to the different altitudinal changes in the leaf traits for carbon balance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".