Ten‐Year Responses of Soil Quality and Conifer Growth to Silvicultural Treatments
Bibliographic record
Abstract
The development of sustainable forestry practices and credible certification systems relies on continuous monitoring of indicators. In the present study, carried out at the Petawawa Research Forest (Ontario, Canada), we evaluated the impacts of three intensive silvicultural treatments: scarification, fertilization, and herbicide treatment, applied alone or in combination—on indicators of organic layer quality, foliar nutrition, and tree growth—10 yr after establishment of eastern white pine ( Pinus strobus L.) and white spruce [ Picea glauca (Moench) Voss] plantations. We compared these 10‐yr results with measurements made 3 to 4 yr after plantation establishment. In both 1989 and 1996, the herbicide treatment had the greatest effect on organic layer quality. In 1996, compared with the no‐treatment control, herbicide application reduced organic C mass by 46%, total N mass by 15%, and acid phosphatase activity by 64%. These negative effects were offset when herbicide was applied in combination with fertilizer. The negative impact of herbicide on microbial biomass C noted in 1990 was no longer evident in 1996. In herbicide‐treated plots, the nitrate‐dominated cycle observed 1989–1990 was replaced by an ammonium‐dominated cycle in 1996. Although herbicide application negatively affected soil quality, it increased tree growth and generally improved foliar nutrition; thus organic layer and tree responses were not correlated. The indicators used were sensitive to changes in the ecosystem over time and signaled soil impacts that could have consequences for long‐term productivity.
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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 | 0.000 | 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".