The Response of Conifer and Broad-Leaved Trees and Shrubs to Wildfire and Clearcut Logging in the Boreal Forests of Central Labrador
Bibliographic record
Abstract
Abstract To assess the differences between forest management and natural disturbance, we retrospectively compared crown cover of woody plant species between burned and clearcut sites after 5, 14, and 27 years of succession. All 16 sites had been dominated by black spruce (Picea mariana) before disturbance. We found no difference in species richness between disturbance types, but richness was lowest on 5-year-old sites for both disturbances. Burned and clearcut sites differed in the cover of woody plant species, differences increasing slightly with time since disturbance. Both balsam fir (Abies balsamea) and black spruce were more abundant on 14- and 27-year-old clearcut plots than burned plots. Black spruce cover was always greater than fir, but the spruce:fir ratio on clearcut plots was lower than on burned plots. Our data suggest that fire and clearcut logging affect postdisturbance succession differently. Contrary to other studies, logging resulted in more commercially valuable black spruce than fire, and broad-leaved woody plants were not in greater abundance on logged sites. However, the persistence of fir through logging suggests that the resulting forest would be of lower commercial value than a pure black spruce forest. North. J. Appl. For. 22(1):35–41.
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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 | 0.001 |
| 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".