Liming has a limited effect on sugar maple – American beech dynamics compared with beech sapling elimination and canopy opening
Bibliographic record
Abstract
Sugar maple (SM, Acer saccharum Marsh.) dominated forests of North America are increasingly affected by many human-induced modifications in environmental conditions. As a remedy, adapted silvicultural treatments are needed. Even though it is generally accepted that SM health is related to soil fertility and even though there is extensive literature on SM – American beech (AB, Fagus grandifolia Ehrh.) regeneration stand dynamics related to light availability, the interaction between these two factors has rarely been studied. Our main objective was thus to verify the possible role of a light–soil interaction on SM–AB stand dynamics. We used a factorial design with three factors (harvest intensity, liming, and AB sapling elimination) to test this interaction. Our results showed that the radial growth of SM and AB tree and sapling growth was positively affected by canopy opening but not by liming. Liming did not favour AB seedlings, whereas it favoured SM seedlings in specific canopy opening situations, confirming, albeit partially, the light–soil interaction hypothesis. Overall, liming had very limited effects on SM–AB stand dynamics compared with canopy opening and AB sapling elimination treatments. We do not advocate the extensive use of liming, as other silvicultural strategies tested provided more promising results to favour SM over AB.
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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".