Modeling paludification and fire impacts on the forest productivity of a managed landscape using valuable indicators: the example of the Clay Belt
Bibliographic record
Abstract
In areas sensitive to forest management, paludification and successive disturbances in boreal forest can affect forest regeneration negatively, sometimes resulting in stand opening. As these negative effects on forest productivity are not fully considered in strategic management planning, a new landscape dynamics model integrating fire, paludification, forest harvesting, and regeneration failure was used to assess these impacts in a large forest management unit (10 828 km2) of northwestern Québec. Two reforestation scenarios, one based on the accessibility of the areas to be treated and the other aimed at restoring all burned and paludified areas to production were compared with one with no intervention. The success of the scenarios was evaluated using the predicted volume harvested, the proportion of closed or opened stands areas, which is an indicator of productivity, and the cost of reforestation and the royalties associated with harvesting. Harvesting the paludified areas without reforesting would lead to a sharp increase in open stands areas (+17.3%). The strategy of reforesting accessible areas is the most promising for achieving sustainable forest management targets. The monitoring of maximum potential volume and the closed forest area as indicators of landscape productivity provides the ability to anticipate problems earlier than with the conventional forest planning indicators.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".