Repeated thinning treatments reduce long-term soil carbon and nitrogen storage: an 87-year study at the Petawawa Research Forest, Canada
Bibliographic record
Abstract
Forest management activities are increasingly analyzed through a lens that quantifies their effects on soil carbon (C) and nitrogen (N) storage because forest soils are an important C sink. Data on the longer-term impacts of repeated interventions are often lacking. At the Petawawa Research Forest, Ontario, Canada, treatments to evaluate the effect of repeated thinnings on wood quality of red pine (Pinus resinosa Aiton) and white pine (Pinus strobus L.) were initiated in 1918 with the first experimental plots in Canada: permanent sample plots 1 (thinned) and 2 (control). In 2005, 16 years after the last thinning in 1989, we observed that repeated thinnings reduced soil C and N stocks in the surface L, F, and Ah horizons. Contrary to our hypotheses, concentrations and stocks of C and N increased in the Bm1 horizon, indicating that these elements could be accumulated in deeper horizons after surface disturbance and potentially increased decomposition associated with thinning. However, total C and N accumulation in the profile to 30 cm contributed to reduced storage (–35% for C and –30% for N). Many forest sites in the Great Lakes Forest Region that are selectively cut repeatedly over decades could experience this level of soil C and N decline.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".