A biophysical approach to delineate a northern limit to commercial forestry: the case of Quebec’s boreal forest
Bibliographic record
Abstract
The boreal forest ecosystem is one of the largest frontier forests of the world, providing many ecological services to society. Boreal forests are also economically important, but forest harvesting and management become increasingly difficult when one moves from south to north in boreal environments. An approach was thus developed to assess the suitability of land units for timber production in a sustainable forest management (SFM) context in the northern boreal forest of Quebec (Canada). This area includes all of Quebec’s spruce – feather moss bioclimatic domain (closed forest), as well as the southern portion of the spruce–lichen bioclimatic domain (open woodland). Four criteria specific to the biophysical aspects of SFM were evaluated in 1114 land districts: physical environment, timber production capacity, forest vulnerability to fire (e.g., probability that it reaches maturity), and conservation of biodiversity. Indicators and acceptability cutoff values were determined for each selected criterion, and a sequential analysis was developed to evaluate if a land district has the potential to be sustainably managed. This analytical process led to the classification of land districts into three categories: slightly sensitive (SFM possible); moderately sensitive (SFM possible under certain conditions); and highly sensitive (SFM not possible). The results show that 354 land districts were highly sensitive, 62 due to physical constraints (7.5% of the area), 130 due to insufficient potential productivity (15.4% of the area), 92 due to insufficient potential productivity to account for the fire risk (13.8% of the area), and 70 due to an insufficient proportion of tall and dense forest habitats (7.7% of the area — biodiversity criterion). This work provides scientific background to proposing a northern limit for forest management activities in Quebec. The developed approach could be useful in other jurisdictions to address similar issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".