Quantifying Harvesting Impacts using Soil Compaction and Disturbance Regimes at a Landscape Scale
Bibliographic record
Abstract
Several indicators have been identified for the conservation and maintenance of soil criterion in the Montreal Protocol. The objective of this study was to use soil compaction and disturbance measures to determine harvesting impacts at a landscape scale in the boreal forest of Saskatchewan. Forest harvesting impacts were studied pre and postharvest for five harvested sites by (i) sampling soil bulk density ( D b ) at prescribed grid‐points, and (ii) measuring soil disturbance regimes on two 30‐m transects at each grid‐point. Mean soil D b in the harvested area increased significantly (8–11%) from pre to postharvest conditions for the two winter‐harvested sites at both the 10‐ and 20‐cm depths, while two of the three summer harvested sites also showed significant D b increases (7–15%) at the 10‐cm depth. Combining all five sites, showed that after harvest 32% of all the grid‐points had an increased D b of >15%. Mean soil D b at a 10‐cm depth for roadways and landings was significantly higher (8–14%) than postharvest D b for postharvest levels at four of the five harvested sites. Surface soil disturbance regimes were higher for the summer‐harvested sites than that for the winter‐harvested sites. Landscape position showed no significant differences in D b between the shoulder, backslope, and footslope positions; however, within each landscape position, significant differences in D b were found between pre and postharvest conditions. Soil D b and soil disturbance regimes measured on a grid basis provided a simple, but reliable method for monitoring soil compaction and disturbance effects from harvesting at a landscape scale.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".