Bibliographic record
Abstract
Moose (Alces alces L.) browsing in Gros Morne National Park has damaged its balsam fir (Abies balsamea (L.) Mill.)dominated forest. A forest estate model was used to evaluate (i) the impacts of moose browsing and woodcutting on forest succession and (ii) strategies of forest restoration through planting and moose population management. The simulation results show that under current heavy browsing pressure growing stock of balsam fir decreases by 38%, but the area of spruce (Picea mariana (Mill.) BSP and P. glauca (Moench) Voss) increases by 32% over a 100-year planning horizon, compared to that under light browsing scenario which is assumed to be similar to the forest outside the Park due to moose population management. Annual allowable cut (AAC) for the Park’s 19 400 ha domestic harvest area is estimated to be around 120 979 m 3 in a light browsing scenario, 21% higher than the sustainable harvest level in a heavy browsing scenario. The model forecasts a 97% reforestation of the Park’s 7 194 ha disturbed area by planting in the heavy browsing scenario, leading to an increase in total forest growing stock by 22% and AAC by 12%. Integration of planting with moose population management could be a more efficient way of restoring forest under high browsing pressure in GMNP.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.257 | 0.174 |
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".