Emulation or Degradation? Evaluating Forest Management Outcomes in Boreal Northeastern Ontario
Bibliographic record
Abstract
Forest degradation has gained global attention for its role in exacerbating biodiversity loss and climate change, yet indicators, baselines, and thresholds of degradation remain under debate. Maintaining key forest characteristics within bounds of natural variability offers a strategy to sustain ecological integrity and to provide potential measures of degradation. We used forest inventories, satellite-derived information, and government planning guidelines to evaluate five potential indicators of forest degradation during 2012-2021 for public forests in boreal northeastern Ontario, Canada. We tested two contrasting hypotheses (natural disturbance emulation vs. timber maximization) by comparing observed values against those from two reference landscapes: one shaped by empirical estimates of natural fire disturbance regimes and one by forest management aimed at maximizing timber volumes. All indicators fell outside bounds of natural variability from natural landscapes and were more consistent with timber maximization. Specifically, compared to natural landscapes, some forest types were disturbed at substantially higher rates; the proportion of forest >100 years old was significantly lower (22.4% on average vs. 53.5% in a natural landscape); and modelled boreal caribou and American marten habitats were highly fragmented and substantially reduced (12% for boreal caribou and 36% for American marten vs. corresponding percentages of 73% vs. 76% in a natural landscape). Government planning targets for natural variability targets also were lower than, and did not overlap with, empirical estimates. Continued degradation of biodiversity and ecological services is likely unless management approaches are altered.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".