The effects of site characteristics on the landscape-level windthrow regime in the North Shore region of Quebec, Canada
Bibliographic record
Abstract
Understanding windthrow is essential for the implementation of ecosystem management, especially in forests with long fire return intervals. Our study describes windthrow dynamics at a landscape scale of the Quebec North Shore region, Canada, and evaluates the effect of some site (soil surface material thickness or deposit thickness, drainage, slope, topography and wind), and stand (dominant species, height and density) characteristics on windthrow probabilities. The SIFORT database, created by the Ministry of Natural Resources and Wildlife of Quebec, the Quebec forest fire control agency and the Quebec forest pest and disease control agency, was used to perform a spatiotemporal analysis of windthrow, according to site and stand characteristics. Windthrow probabilities were influenced by topographic exposure (topex), slope classes and deposit thickness. Windthrow probabilities increased with topographic exposure. Windthrow occurrence was highest when deposit was thick (more than 1 m) and slope class was medium (from 15 to 30 per cent). Finally, this study has shown the importance of partial windthrow at the landscape scale in the North Shore region. Thus our results suggest that from an ecosystem management perspective, clear-cutting must be partly replaced by partial cuts, in order to emulate the regional dynamics of partial windthrow.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".