Emergent freeze and fire disturbance dynamics in temperate rainforests
Bibliographic record
Abstract
Abstract The coastal temperate rainforests of South and North America are part of the most biomass dense forest biome on the planet. They are also subject to rapid climatic shifts and, subsequently, new disturbance processes – snow loss‐driven mortality and the emergence of fire in historically non‐fire‐exposed areas. Here, we compare and contrast Southern and Northern Hemisphere coastal temperate rainforests of the Americas, two of the largest examples of the biome, via synthesis of current literature, future climate expectations and new downscaling of a global fire model. In terms of snow loss, a rapid decline in winter snow is leading to mass mortality of certain conifer species in the Northern Hemisphere rainforests. High‐elevation Southern Hemisphere forests, which are beginning to see similar declines in snow, may be vulnerable in the future, especially bogs and high‐water content soils. Southern Hemisphere forests are seeing the invasion of fire as an ecological force at mid‐to‐high latitudes, a shift not yet observed in the north but which may become more prominent with ongoing climate change. We suggest that research should focus on the flammability of seral vegetation and bogs under future climate scenarios in both regions. By comparing these two drivers of change across similar gradients in the Northern and Southern Hemispheres, this work points to the potential for emerging change in unexpected places in both regions. There is a clear benefit to conceptualising the coastal temperate rainforests of the Americas as two examples of the biome which can inform the other, as change is proceeding in similar directions but at different rates in each region.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".