Incorporating Insect and Wind Disturbances in a Natural Disturbance-Based Management Framework for the Boreal Forest
Bibliographic record
Abstract
Natural disturbances are fundamental to forest ecosystem dynamics and have been used for two decades to improve forest management, notably in the boreal forest. Initially based on fire regimes, there is now a need to extend the concept to include other types of disturbances as they can greatly contribute to forest dynamics in some regions of the boreal zone. Here we review the main descriptors—that is, the severity, specificity, spatial and temporal descriptors and legacies, of windthrow and spruce bud worm outbreak disturbance regimes in boreal forests—in order to facilitate incorporating them into a natural disturbance-based forest management framework. We also describe the biological legacies that are generated by these disturbances. Temporal and spatial descriptors characterising both disturbance types are generally variable in time and space. This makes them difficult to reproduce in an ecosystem management framework. However, severity and specificity descriptors may provide a template upon which policies for maintaining post harvesting and salvage logging biological legacies can be based. In a context in which management mainly targets mature and old-growth stages, integrating insect and wind disturbances in a management framework is an important goal, as these disturbances contribute to creating heterogeneity in mature and old-growth forest characteristics.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".