Influence of recent fire season and severity on black spruce regeneration in spruce–moss forests of Quebec, Canada<sup>1</sup>This article is one of a selection of papers from the 7th International Conference on Disturbance Dynamics in Boreal Forests.
Bibliographic record
Abstract
Postfire regeneration problems compromise the maintenance of closed-crown forests of eastern Canada, often shifting toward lichen woodlands. Compounded disturbances like successive fires or insect outbreaks followed by fire may be responsible for this shift. Leaving behind unfavourable seedbeds for the germination of black spruce ( Picea mariana (Mill.) B.S.P.) seeds, low-severity fires may also be involved in this transformation. The severity of recent fires and their impact on black spruce regeneration were evaluated using 13 stands burned in spring or summer within the closed-crown forest in Quebec during the last 20 years. Two ecological indicators were used to characterize fire severity: thickness of residual organic material and recovery of plant species. Regardless of the season, the ground of all burned stands was covered with a thick layer of residual organic matter. Blackened organic matter and ericaceous vegetation, indicating the passage of a low-severity fire, were widespread in all sites whereas acrocarpous mosses and bare mineral soil, indicating the passage of a severe fire, were uncommon. The preponderance of the thick layer of residual organic material blackened at the surface can explain the failure of regeneration in most studied sites. Low-severity fires are thus among factors probably involved in the expansion of lichen woodlands within the closed-crown forest.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".