Long-term decline of sugar maple following forest harvest, Hubbard Brook Experimental Forest, New Hampshire
Bibliographic record
Abstract
Forest harvesting can impact site quality by removing essential nutrients, exacerbating the effects of historic base cation losses associated with acid deposition. We studied the 30-year trajectory of forest recovery from clearcutting (whole-tree harvest (WTH)) in a forest originally dominated by sugar maple (Acer saccharum Marsh.). At both the watershed scale (21.9 ha) and the “detailed” plot scale (1 m2), a dramatic decline of sugar maple was observed, along with maintenance of American beech (Fagus grandifolia Ehrh.) and an increase in birch, mainly yellow birch (Betula alleghaniensis Britt.). Many of the “detailed” plots where sugar maple failed to recruit became unoccupied rather than being “won” by another species. The decline of sugar maple was most severe in the upper elevation zones of the watershed, where low base status (especially Ca) of the soils was a likely driver. The results support previous studies indicating that regeneration by sugar maple is severely compromised on base cation depleted soils. Lower survival of seedlings for sugar maple emphasized the importance of maintaining advance regeneration to favor desired species such as sugar maple. Foresters should consider that sites with low base saturation and exchangeable Ca are likely to exhibit regeneration failure for sugar maple in the long term, even those with initial dominance by this species.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".