Mechanical site preparation impacts on soil properties and vegetation communities in the Northwest Territories
Bibliographic record
Abstract
Greater utilization of hardwood species and societal concerns over maintenance of ecological integrity have provided impetus for forest managers to consider alternative silvicultural practices in boreal mixedwood forests. The objective of this study was to quantify the effects of five mechanical site preparation (MSP) treatments on soil properties and understory vegetation of mixedwood stands in the Northwest Territories (NWT). Soil and understory vegetation conditions in treatments (3 years post-MSP treatment) and adjacent uncut forest controls were sampled. Significant Shearblade Grizz R-ex and Shearblade treatment soil property effects were consistently found. Increases in bulk density (307%) and decreases for total organic carbon (92%); total nitrogen (86%); cation exchange capacity (74%); and exchangeable calcium (72%), magnesium (67%), and potassium (75%) in the soil surface (012.8 cm) were observed. Increases in mineral soil pH (1.0 units), total organic carbon (94%), cation exchange capacity (20%), and exchangeable calcium (35%) and magnesium (56%) were also found. Dissimilarity of the understory community with that of the uncut forest increased as follows: uncut forest < harvested only < MeriCrusher = Grizz R-ex < Shearblade Meri-Crusher < Shearblade Grizz R-ex < Shearblade. This research suggests that winter shearblading should be utilized only where it is necessary to achieve specific management objectives.
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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) | 0.002 | 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".