Current capacity to conduct ecologically sustainable forest management in northeastern Canada reveals challenges for conservation of biodiversity
Bibliographic record
Abstract
Long-term exploitation of boreal ecosystems often results in a reduced range of ecological conditions that threatens several species. In most boreal jurisdictions, the northern extent of commercial forestry corresponds to economical rather than ecological considerations. Our general objective is to offer guidance for sustainable boreal forest management by using a biodiversity criterion based on three indicators. The first two indicators are part of a coarse filter referring to the proportion and fragmentation of tall, dense forest habitats, whereas a third one uses a fine filter for specific requirements of boreal caribou. We applied the methodology with and without anthropogenic disturbances on 1114 land districts to contrast the preindustrial potential and current capacity of Quebec’s boreal forest to support forest management. Originally, 826 districts (74%) were above the 20% cutoff value for the minimum proportion of tall, dense forest habitats. Among the 567 districts currently under forest management, 45 did not reach this value because of past anthropogenic disturbances. Originally, 88% of the districts had sufficient undisturbed habitats to maintain caribou populations, but anthropogenic disturbances reduced this proportion to 51%. The proposed methodology could contribute to delineating areas where sustainable forest management can be implemented. Our results also clearly show that management targets of the last decades were insufficient to prevent loss of habitats below strict minimum ecological thresholds. Our approach offers a general framework that could be adapted to other forested regions to attain similar biodiversity conservation objectives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".