A multiscale analysis of the effects of alternative silvicultural treatments on windthrow within balsam fir dominated stands
Bibliographic record
Abstract
Boreal ecosystem functioning is largely controlled by disturbance dynamics. There have been efforts at adapting forest management approaches to emulate natural disturbance effects, as this is expected to maintain ecosystem resilience. In many instances, this involves resorting to partial cutting strategies that are likely to increase windthrow losses. The objective of this study was to determine the effects of alternative silvicultural practices on windthrow damage and how these effects vary with the scale of treatment. The study was conducted in the Quebec North Shore region (Canada), an area dominated by balsam fir (Abies balsamea (L.) Mill.) and accompanied by black spruce (Picea mariana (Mill) B.S.P.). Four different silvicultural treatments (overstory removal, heavy partial cutting, and two patterns of selection cutting) and control areas were implemented in 2004 and 2005. The experiment used a nested approach where treatment at the plot level was independent and yet nested within the block-level treatment. At the block level, treatments were applied over 10–20 ha units, leaving a small portion of the block for a smaller application of each treatment (plot scale, 2500 m2). Inventory was carried out before harvesting and monitoring was done yearly after harvesting, with the aim to better understand the plot- and block-level factors that drive windthrow damage levels and the effects of alternative silvicultural treatments. Results after 6–7 years show that basal area proportion windthrown differs substantially between treatments, as well as between treated sites and control sites. Windthrow levels were higher under heavy cuts relative to selection cuts and also increased with balsam fir proportion. Windthrow proportions were better correlated to block-level treatment than plot-level treatment, showing that the environment surrounding the treated plot can have an important effect on windthrow losses. Overall, the selection cutting system, particularly SC2, retains the most green-tree basal area and thus best meets the management objective of retaining old-growth attributes. A simple empirical model was calibrated that could aid in hazard rating.
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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".