Post-wildfire seedbeds and tree establishment in the southern mixedwood boreal forest
Bibliographic record
Abstract
We studied the post-wildfire establishment of jack pine (Pinus banksiana Lamb.), black spruce (Picea mariana (Mill.) BSP), and white spruce (Picea glauca (Moench) Voss) in the southern mixedwood boreal forest of Saskatchewan, Canada. The major objective of the study was to determine the influence of post-wildfire seedbed types on the juvenile survivorship of trees. Through a combination of permanent plots and sowing experiments, we demonstrated that mineral soil, thin Polytrichum Hedw. moss, and humus are much more favorable than the organic fermentation (O f ) and litter seedbeds. We also show that differences among seedbeds are significantly more important than differences among species. In addition, the first year of a cohort has the highest rate of mortality, about 85% on mineral and humus seedbeds and 98% on O f seedbeds; differences in age-specific survivorship between seedbeds become muted by the end of the second year, and survivorship rates approach 1 by the end of the third summer. Finally, age structures showed that germination rates of black spruce and jack pine were very low the initial summer of the fire; that there was a peak in recruitment in the first post-fire summer; and that by the fourth year the recruitment declined to nearly zero.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".