Decline of regional ecological integrity: Loss, distribution and natural heritage value of roadless areas in Ontario, Canada
Bibliographic record
Abstract
Only eight years remain to increase nature protection by 20 million ha in Ontario from 10.7% to 30% by 2030 to meet government commitments. Rapid identification and assessment of unprotected roadless areas (RAs) would help to achieve this goal by focussing natural heritage protection efforts in areas with high ecological and conservation value. In Ontario, little is known about the location and extent of RAs, thus the purpose of this study was to map and describe RAs in Ontario, and to discuss their value. Total length of roads in Ontario increased from ∼90,000 km in 1916 to ∼607,500 km in 2020 – an increase of ∼517,500 km (675%) over 104 years. Within Ontario's managed forest region (MFR; excludes the Far North), RAs declined from ∼34 million ha in 1916 to ∼18.5 million ha in 2020 resulting in a loss of ∼15.5 million ha reducing RA cover in the region to 35.6%. Doubling logging production by 2030 per a new Ontario policy could reduce RAs by as much as 20% to ∼14.8 million ha by 2030, potentially resulting in their depletion between 2090 and 2100. In 1880, woodland caribou occupied ∼43 million ha in Ontario's MFR, which declined to ∼10 million ha by 1990. Caribou occupancy in this region could be eliminated by ∼2024 and extirpated from all of Ontario by 2070. If all remaining RAs in the MFR were designated as protected areas, Ontario would achieve 92.7% of the 30 × 30 goal. RAs in Ontario continue to be degraded, fragmented and eliminated.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".