Red hot maples: <i>Acer rubrum</i> first-year phenology and growth responses to soil warming
Bibliographic record
Abstract
Microhabitat environmental conditions are an important filter for seedling establishment, controlling the availability of optimal recruitment sites. Understanding how tree seedlings respond to warming soil temperature is critical for predicting population recruitment in the future hardwood forests of northeastern North America, particularly as environmental conditions and thus optimal microhabitat availabilities change. We examined the effect of soil warming of 5 °C during the first growing season on germination, survival, phenology, growth, and stem and root biomass allocation in Acer rubrum L. (red maple) seedlings. While there was no effect of soil warming on germination or survival, seedlings growing in warmer soils demonstrated significantly accelerated leaf expansion, delayed autumn leaf senescence, and an extended leaf production period. Further, seedlings growing in warmer soils showed larger leaf area and stem and root structures at the end of the first growing season, with no evidence of biomass allocation trade-offs. Results suggest that A. rubrum seedlings can capitalize on soil warming by adjusting leaf phenology and leaf production, resulting in a longer period of carbon uptake and leading to higher overall biomass. The absence of growth allocation trade-offs suggests that A. rubrum will respond positively to increasing soil temperatures in northeastern forests, at least in the early life stages.
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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".