The effects of moose ( Alces alces L.) on hemlock ( Tsuga canadensis (L.) Carr.) seedling establishment in Algonquin Provincial Park, Ontario, Canada
Bibliographic record
Abstract
The effects of moose on eastern hemlock (Tsuga canadensis) natural seedling establishment in Algonquin Provincial Park, Ontario, were examined. Two thousand seedlings were tagged on 56 sites in 1992 and monitored for six years. Initial data collected included seedling height, browsing history and percent crown closure. At the end of the growing season of the following six years, heights of these seedlings were remeasured; damages on seedlings were assessed according to browsing, physical, logging and unknown factors. After six years, 10.4% of the seedlings had died. Browsing caused 60% of the mortality, followed by unknown factors (18%). Growth rates of healthy, unbrowsed seedlings were significantly affected by initial height, health and percent crown closure. Growth was best at 60% crown closure and least at 80% to 100% crown closure. Healthy, undamaged seedlings grew better than seedlings with browse damage, dieback or both. Mean height losses were 11.1 cm with each browsing incident. Growth rates indicated that seedlings may need to avoid being browsed for up to 30 years to ensure leaders are out of the reach of moose. A negative correlation between moose density and browsing suggested that low moose density in recent years may provide a better opportunity for establishment and canopy recruitment.
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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".