Boreal forest soil biotic communities are affected by harvesting, site preparation with no additional effects of higher biomass removal 5 years post-harvest
Bibliographic record
Abstract
The market for forest products has diversified to include biomass energy products sourced from materials that were typically left on-site after harvest. In this study we examined whether intensified biomass removal following harvest will effect site soil biological diversity and metabolic functioning, and how decisions surrounding sampling, in sites prepared with disc-trenching soil scarification, influence the effect assessment of harvesting activities. We compared the influence of harvesting, below-ground biomass removal, stem only versus full tree harvesting, micro-topographic features and seasonal effects on soil biota. We assessed soil biotic changes using potential enzyme activity assays and metabarcoding using targeted primers for four groups (arthropods, bacteria, fungi and broad eukaryotes). We found that harvesting and forest floor removal had strong influences on soil biotic communities, with changes that generally favoured more general soil saprotrophy and reductions in plant-associated organisms. Disc-trenching created differences between micro-topographic features where organic soil was redistributed and trenches, where it was removed. We found no significant difference to how seasonal variation effected communities in harvested sites, compared to unharvested sites. These results show that there is significant site heterogeneity due to disc scarification, and that the sampling micro-topographic features should be considered during study design. Additional studies are needed to assess the effects of higher biomass removal on these features as these sites age.
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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) | 0.001 | 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".