Harvesting impacts on soil and understory vegetation: the influence of season of harvest and within-site disturbance patterns on clear-cut aspen stands in Minnesota
Bibliographic record
Abstract
We investigated impacts of harvesting on soil disturbance and vegetation in the aspen cover type of northern Minnesota, United States. The soil disturbance (resistance to penetration) and understory vegetation were characterized for 19 sites on five 60-m2 plots placed along a disturbance gradient: landings (high harvesting traffic), skid trails (intermediate harvesting traffic), and areas off skid trails (low to no harvesting traffic). Penetration levels were quite variable, but they still indicated that within-site responses to disturbance patterns created by clear-cut harvesting were not uniform. In general, soil disturbance and understory species composition within landings were similar to those with skid trails. The soil disturbance and vegetation composition of these two levels differed from those of the low-disturbance plots (off skid trails), indicating that removing trees alone did not affect vegetation composition as much as creating an established skid trail, regardless of harvest timing. However, sites with more variable species composition (winter-harvested sites) and lower disturbance levels were less altered than sites with likely lower initial diversity (summer-harvested sites). The results suggest that it is important for recovery of understory plant communities to not only limit the amount and level of disturbances but also consider the spatial layout of harvesting, thus maintaining a spatially connected network of remnant forest patches large enough to contain interior forest species.
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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".