Aspen regeneration, forage production, and soil compaction on harvested and grazed boreal aspen stands
Bibliographic record
Abstract
The objective of our study was to determine the effects of timber harvesting and cattle grazing on aspen regeneration, forage production, and soil compaction on aspen cutblocks in the Peace River region of British Columbia. This project was carried out on a long-term study site established 5 km south of Dawson Creek, B.C. Samples were collected and vegetation was assessed during the summer of 2002. Summer and winter harvesting significantly increased aspen stem density relative to unharvested plots, whereas 4 years of cattle grazing had no significant impact on stem density. Inter-tree spacing remained above the postulated minimum of 60-80 cm, indicating that livestock can access the stand. Timber harvesting increased forage production by 69%, while grazing had no effect on forage production. Soil penetration resistance was similar for three harvesting treatments down to a 21 cm depth, while between 21 and 60 cm penetration resistance was consistently the highest on summer-harvested plots, followed by winter-harvested and unharvested plots. Grazing had no impact on soil penetration resistance. The results of this study support the view that cattle grazing and aspen harvesting are complementary land uses for aspen cutblocks on similar sites in the Peace River region; however, proper planning is required to avoid potential cattle distribution problems.
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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 | 0.001 | 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".