Variations in northern white-cedar (<i>Thuja occidentalis</i>) regeneration following operational selection cutting in mixedwood stands of western Quebec
Bibliographic record
Abstract
Poorly adapted silvicultural practices and increases in white-tailed deer (Odocoileus virginianus (Zimmerman, 1780)) populations have most likely contributed to the decline of northern white-cedar (Thuja occidentalis L.) in many regions of eastern North America. Selection cutting has been suggested to regenerate northern white-cedar in mixedwood stands, but the approach has not yet been validated in an operational framework. The objective of this study was to determine how local variations in stand condition and treatment application influence northern white-cedar regeneration at an operational scale in mixedwood stands. Seventy treated and control permanent plots, having at least 10% of basal area in cedar, were selected in an operational harvesting site. A regeneration survey was conducted in 2014, 15 to 20 years after harvesting, and data on harvested trees and tree cover, as well as regeneration state and abundance, were collected. Results indicate that selection cutting allows for the establishment of northern white-cedar when deer densities are low, which was the case in the study sites. However, abundance of seed trees nearby, harvesting intensity, competition, and availability of establishment microsites influenced abundance, growth, and recruitment of northern white-cedar seedlings and saplings in the residual stand. Deer browsing had no effect on regeneration.
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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 | 0.001 | 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".