Effect of Browsing, Seedbed, and Competition on the Development of Yellow Birch Seedlings in High-Graded Stands
Bibliographic record
Abstract
Abstract Many hardwood or mixedwood stands of northeastern North America have been high graded in the past and need restoration treatments to bring them back to an acceptable level of production. Even when early seedling establishment can be secured, further development may be compromised bymany factors. This study looks at the effect of seedbed, browsing, and competition on the growth and survival of yellow birch (Betula alleghaniensis Britt.) seedlings that became established after a brushing and scarification treatment applied in high-graded mixedwood stands of Quebec,Canada. The seedbed types studied include 1-m-wide scarified patches, 2-m-wide scarified patches, and mounds. Browsing impact was assessed by placing fences around half of the plots. Half of the plots were released from competing vegetation. Browsing by hare (Lepus americanus) was seenas a major factor controlling seedling development between 3 and 6 years after scarification. It reduced both survival and growth and obscured the effect of other factors. In the absence of browsing, competition had a major effect on mounds but not on scarified patches. Mounds were found tohave the best growth potential when competition and browsing were controlled. The scarified patches had the best growth when competition and browsing were allowed. Even though mortality was somewhat higher on scarified patches, initial densities were very high and still provide more seedlingsthan required.
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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) | 0.001 | 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".