Warming adversely affects density but not growth of balsam fir regeneration across a climatic gradient in the Acadian Forest Region of eastern Canada
Bibliographic record
Abstract
Balsam fir ( Abies balsamea (L.) Mill.) is projected to decline in the Acadian Forest Region under climate change. We hypothesized that along a 700 km latitudinal gradient with increasing mean annual temperature from New Brunswick to Nova Scotia, Canada, balsam fir regeneration density and growth rates would (1) decline with warming and (2) decline more in immature than in mature stands due to greater exposure to adverse climate conditions. Regeneration density, height growth, and lateral branch growth were measured in 30 mature and 28 immature plots and related to climate and stand variables using generalized additive models. Balsam fir regeneration density was negatively affected by increased mean annual temperature and increased with summer precipitation, supporting our first hypothesis, but varied by height class. Canopy cover significantly affected seedling density, but its effect varied by substrate type (coniferous litter and dry moss litter), with seedling density being greatest under low canopy cover for large seedlings and greatest at intermediate canopy cover for small seedlings. Seedling growth rates were determined by height class, canopy cover, precipitation, and coniferous litter cover. Stand maturity did not significantly affect fir regeneration density or growth rates. Our results indicate that over time, increasing mean annual temperature may reduce balsam fir regeneration density within the Acadian Forest Region, while the marginal summer precipitation increases may lead to small gains in growth.
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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 | 0.001 | 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".