The benefits of seed banking for red maple (<i>Acer rubrum</i>): maximizing seedling recruitment
Bibliographic record
Abstract
Seed banking is assumed to be unimportant for temperate trees, because their seeds are short-lived in soils. However, even short-term seed banking could increase recruitment and affect population dynamics of seed-banking trees. To investigate this possibility, we examined early life-history stages of red maple (Acer rubrum L.), an abundant seed-banking tree in eastern forests. We found that seed banking benefits red maple by increasing germination when seedling survival is likely. Most red maple seeds germinate soon after spring dispersal, when seedling survival is high, or postpone germination to the following growing season, once seedling survival becomes less likely late in the summer. This occurs because seed dormancy increases during the growing season, matching a concurrent decrease in seedling survival. Our results and those of other studies suggest seed dormancy is increased by the same environmental factors (low light and low moisture) that also decrease seedling survival. We speculate that early life-history traits, including seed banking, may have contributed to this species' increased abundance in eastern deciduous forests in the last century.
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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".