Compensatory mechanisms for reproductive costs in the dioecious tree <i>Salix integra</i>
Bibliographic record
Abstract
In dioecious plants, females often incur greater reproductive costs than males, owing to the production of seed. This has led to evolution of cost-compensatory mechanisms in females. In trees, however, reproductive cost and compensatory mechanisms are not always detectable, probably because costs and compensation traits change with time, plant size, and modular level (i.e., individual shoots and whole plants). Herein we investigated sex-specific reproductive allocation, growth, and carbon acquisition mechanisms at different hierarchical levels in a dioecious tree, Salix integra Thunb. At both shoot and whole-plant levels, females invested more resources into reproduction than males, but without an associated reduction in vegetative growth, suggesting compensatory mechanisms in females. Females had greater biomass allocation to photosynthetic organs and higher photosynthetic rates than males. Although photosynthetic rates decreased with age, higher shoot turnover in females maintained higher productivity by consistently locating new leaves in favourable light. Females had larger areas of leafy bracts beneath peduncles, suggesting a reduction in the translocation distance of the assimilation to mature seeds. Females meet greater reproductive costs not by reducing growth, but by increasing the carbon uptake ability of different modular units in well-illuminated microenvironments.
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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) | 0.001 | 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".