Ecological Decline and Roadless Habitat Restoration after Two Centuries of Multiple-Use Management in Algonquin Park, Ontario, Canada
Bibliographic record
Abstract
Globally, timber production continues to dominate multiple-use forest management despite evidence from many managed landscapes that ecological integrity and biodiversity are not being sustained under that land-use model. This includes Algonquin Park where two centuries of road building, logging, and aggregate mining have contributed to a ~82% (6,200 km2) reduction of unlogged, roadless (>1km from roads) habitat at a mean decline rate of 32 km2/yr. There are at least ~5,500 km of roads that fragment Algonquin Park into 732 roadless habitats covering 18% of the Park's area. Almost 40,000 ha of these habitats are unprotected from logging. Decline of roadless habitat in Algonquin has contributed to the impairment of ecological integrity and decline of at least 33 species across all trophic levels, including at least 17 species-at-risk. Restoring the natural Algonquin Park landscape would result in job losses, however, data suggest that new recreation-tourism and research-education jobs would help to offset these losses. A new agency could build on existing infrastructure to monitor, research, educate about, maintain, and restore biodiversity and recreational resources in the greater Algonquin Park Region, with the park as the central hub. Restoration could be focussed on roadless areas as an “integrative” indicator of ecological integrity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".