Effects of fire severity on early survival and growth of planted jack pine, black spruce and white spruce
Bibliographic record
Abstract
Two boreal mixedwood stands burned by the 1999 Black River wildfire in southeastern Manitoba, Canada were selected to study the effect of fire severity on early survival and growth of planted jack pine (Pinus banksiana), black spruce (Picea mariana) and white spruce (Picea glauca) seedlings. In each stand, three fire severity classes (scorched, lightly burned, and severely burned) were identified based on the degree of forest floor consumption. Fire severity was not a significant factor on mortality. No mortality difference was found among species, except for year 5 when jack pine had significantly higher mortality than both black spruce and white spruce. Jack pine and black spruce had their highest mortality in year 4, while white spruce had its highest mortality in year 1. Under natural competition, seedling growth increased with increasing fire severity. When competition was removed, fire severity did not affect seedling growth. Regardless of fire severity and competition, jack pine had better diameter and height growth than black spruce, which, in turn, grew slightly taller than white spruce. Planted seedlings faced less intense vegetation competition on severely burned plots compared to scorched or lightly burned plots. Regardless of fire severity and species, competition increased with time since planting. Our study results indicate that planting immediately after a wildfire is a viable option to establish conifer components on burned boreal mixedwood stands. Key words: fire severity, plantation, regeneration, Pinus banksiana, Picea mariana, Picea glauca
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".