Development of Northern White-Cedar Regeneration Following Partial Cutting, with and without Deer Browsing
Bibliographic record
Abstract
Northern white-cedar (Thuja occidentalis L.) is an important commercial species with a high wildlife value, both as a food source and habitat for many bird and mammal species. Concerns have been expressed about its decreasing abundance across its range, and especially in mixedwood stands, where it has to compete with several other species and can suffer from heavy browsing. In this study, we quantified the development of natural northern white-cedar seedlings and saplings under various partial cutting regimes, with and without white-tailed deer (Odocoileus virgianus Zimmerman) browsing, in three selected sites in Quebec (Canada) and in Maine (USA). Our data show that northern white-cedar regeneration was present in all studied stands, but that only a few stems were taller than 30 cm on the two sites with high densities of deer. In the absence of heavy browsing, stems reached a height of 30 cm in 11 years, and 130 cm in 28 years. Height growth of northern white-cedar regeneration increased with canopy light transmittance, while ground-level diameter increment increased after partial cutting. This suggests that partial cutting can be used in mixedwood stands to release natural northern white-cedar regeneration, but also that the recruitment of northern white-cedar seedlings to larger size classes constitutes a major challenge in stands subject to heavy deer browsing.
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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".