Twenty-five years post-treatment conifer responses to silviculture on a<i>Kalmia</i>-dominated site in eastern Canada
Bibliographic record
Abstract
Research has demonstrated the potential of soil scarification, fertilization, and herbicide application to improve conifer seedling establishment and early growth. However, tree responses to and interactions among silvicultural treatments vary, making it difficult to predict mid- and long-term impacts of silviculture on stand productivity. We thus evaluated the 25-year effects of scarification and herbicide–fertilization combinations on black spruce (Picea mariana), jack pine (Pinus banksiana) and tamarack (Larix laricina) planted on a Kalmia angustifolia-dominated site. Our results show that the effects of scarification and herbicide–fertilization combinations diverged among species. Black spruce was the most responsive species to scarification for height and diameter at breast height. The combination of herbicide and fertilization treatments still had significantly positive effects on the long-term height and diameter growth of all species. Silvicultural treatments resulted in significant reductions in rotation length (based on height) when compared to height in nontreated-plots, depending on the species; reductions in years to attain a given height were greater for black spruce than for the other species. Our results illustrate the need to take species autecology into account when predicting productivity gains associated with early silviculture, and to provide managers with specific guidelines for the reforestation of ericaceous-dominated sites in Canadian boreal ecosystems.
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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) | 0.001 | 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".