Understory vascular plant responses to retention harvesting with and without prescribed fire
Bibliographic record
Abstract
Wildfire is the predominant natural disturbance in the boreal forests of western Canada. Natural disturbance based forest management involves the use of retention harvesting to retain stand structural diversity after harvest; however, unlike fire, this partial harvesting technique does not cause combustion of the forest floor. Application of prescribed burning to areas treated with retention harvesting might emulate the influence of wildfires more effectively than harvesting alone. We compared understory vascular plant diversity, abundance, and composition between forest stands subjected to dispersed retention harvesting (10% retention) with and without prescribed burning 1, 6, and 11 or 12 years after burning. Untreated forest was included as a reference. Research was conducted in conifer-dominated, mixedwood, and deciduous-dominated boreal forest stands in northwestern Alberta, Canada. In deciduous-dominated stands, burned areas of retention harvested stands had higher species richness and greater cover than did unburned areas. In all three forest cover types, effects of harvest with and without burn on species richness, cover, and composition were still evident a decade after disturbance. Fire-adapted species benefited most from the prescribed burn treatment. The combination of prescribed burning with retention harvesting can be considered a useful option in forest management that aims to emulate natural disturbance.
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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 | 0.000 |
| 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".