Forests, fire, and fish: Policy pathways to manage forests for wildfire resilience, salmon recovery, and watershed security
Bibliographic record
Abstract
As a high-latitude nation, Canada is warming at more than twice the global average (Environment and Climate Change Canada, 2019). This trend is driving higher temperatures, reduced precipitation (Bush and Lemmon, 2019), prolonged droughts, and increased lightning frequency (Romps et al., 2014). In British Columbia, industrial forestry practices have reshaped forest ecosystems. Clear-cut logging, the removal of old-growth trees, and the suppression of broadleaf vegetation have resulted in homogenous, even-aged stands with little species diversity. Coupled with over a century of fire exclusion, landscapes across British Columbia have high fuel loads and reduced ecological resilience, making them increasingly prone to high-severity wildfires. Wild Pacific salmon are foundational to ecosystems across British Columbia and have adapted to fire regimes over their evolutionary history. Pacific salmon, and aquatic ecosystems more broadly, can experience both positive and negative effects from wildfire through modifications to habitat complexity, streamflow, and water temperature. However, contemporary fire regimes that are characterized by more frequent, intense, and severe wildfires may exceed their adaptive capacity. Exacerbating this challenge is a suite of cumulative effects that interact across multiple scales, including: increased marine and freshwater temperatures, habitat loss and degradation, barriers to fish passage, fisheries exploitation, and fish farm and hatchery interactions. Collectively, these stressors undermine recovery and make the viability of some salmon populations uncertain in a hotter, drier climate.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".