Does intensified boreal forest harvesting impact soil microbial community structure and function?
Bibliographic record
Abstract
Intensified biomass harvesting in northern forests could potentially negatively impact soils. This study measured microbial community structure and function to assess the impacts of intensified biomass removal on soil from a managed northern jack pine (Pinus banksiana Lamb.) forest in Ontario, Canada. Four clear-cut harvesting removal intensities were compared with uncut controls and mature, fire-regenerated forest reference plots: stem-only removal, full-tree biomass removal, full-tree biomass with stump removal, and full-tree biomass with stump removal and soil blading that eliminated all aboveground and much belowground organic matter. A nearby recently burned forest site, representing common natural disturbance in the region, was also studied. Within the first two years after harvesting, there were significant differences in community structure and degradation of various C compounds among all harvested and unharvested sites, but little difference in communities across the different harvest intensities. Communities within the fire site were not comparable with those of harvested treatments, indicating that clear-cut logging may not initially produce an ecologically comparable disturbance with that of fire, although this conclusion is based on only one fire disturbance site. In the two years after harvesting, an important time for seedling establishment in managed forest systems, it appears that intensification of harvesting does not further disrupt microbial community structure and functioning beyond impacts from current harvest practices.
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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) | 0.000 | 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".