Six Decades of Forest Inventory Data Highlight Decline of Sugar Maple (<i>Acer saccharum</i>) Sapling Abundance in Eastern Canada
Bibliographic record
Abstract
ABSTRACT Six decades of temporal changes in the abundance of sugar maple (Acer saccharum Marsh.) were investigated using a network of multi‐agency ground plots (MAGPlots) located across Ontario, Québec, and New Brunswick, Canada. Based on a composite dataset of nearly 400 plots mainly composed of sugar maple trees (≥ 50% basal area, m2 ha−1), results showed that the relative abundance (% total sapling basal area) of sugar maple saplings declined significantly over time. On average, the relative abundance of sugar maple saplings decreased significantly between 1970 and 2022. Out of a wide range of potential explanatory variables, including stand conditions, harvest intensity (0%–92% basal area removal), regional ecozones, and climate variables, the relative abundance of American beech (Fagus grandifolia Ehrh.) saplings was the only variable that had a negative effect on the relative abundance of sugar maple saplings. The plot‐specific distribution of change between the final and initial measurements over time revealed that many plots showing a decline in relative sugar maple sapling abundance also experienced an increase in relative American beech sapling abundance. The lack of differences between harvested and unharvested plots suggests that beech sapling control in the understory and soil liming treatments may be required to help promote sugar maple regeneration and development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".