Leaf life span optimizes annual biomass production rather than plant photosynthetic capacity in an evergreen shrub
Bibliographic record
Abstract
SUMMARY: *Owing to nitrogen (N) translocation towards new leaves, the shedding of old leaves can increase the whole-plant carbon gain. It occurs when their photosynthetic nitrogen use efficiency (PNUE) declines below a given threshold. *Here, we investigated variations in net photosynthetic capacity (A(max)), N resorption and PNUE in populations of Rhododendron ferrugineum presenting different mean leaf life spans (LLS). *Both populations had comparable annual leaf surface area production and A(max) across leaf-age cohorts. Branch photosynthetic capacity was up to 95% higher in the population with the longer LLS mainly because of the high contribution of old leaves to the total leaf area. Despite lower N concentrations, old leaves maintained relatively high A(max) and consequently PNUE that were higher than or similar to the values found in current-year leaves. *As the ratio of PNUE in old to PNUE in new leaves was always higher than the fraction of leaf N resorbed during leaf shedding, we concluded that leaf shedding did not improve plant photosynthetic capacity. We suggest that in R. ferrugineum, leaf shedding is mainly controlled by the leaf storage function and, therefore, that models aiming to explain LLS should not only consider the leaf carbon assimilation function, particularly in nutrient-poor habitats.
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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".