Response of vegetation and soil biological properties to soil deformation in logging trails of drained boreal peatland forests
Bibliographic record
Abstract
In the boreal region, peatland forests are a significant resource of timber. Under pressure from a growing bioeconomy and climate change, timber harvesting is increasingly occurring over unfrozen soils. This is likely to cause disturbance in the soil biogeochemistry. We studied the impact of machinery-induced soil disturbance on the vegetation, microbes, and soil biogeochemistry of drained boreal peatland forests caused by machinery traffic during thinning operations. To assess potential recovery, we sampled six sites that ranged in time since thinning from a few months to 15 years. Soil disturbance directly decreased moss biomass and led to an increase in sedge cover and a decrease in root production. Moreover, soil CO2 production potential, and soil CO2 and CH4 concentrations were greater in recently disturbed areas than in the control areas. In contrast, CO2 and CH4 emissions, microbial biomass and structure, and the decomposition rate of cellulose appeared to be uncoupled and did not show signs of impact. While the impacted properties varied in their rate of recovery, they all fully recovered within 15 years covered by our chronosequence study. Conclusively, drained boreal peatlands appeared to have high biological resilience to soil disturbance caused by forest machinery during thinning operations.
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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) | 0.000 | 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".