Climate, wildfire, and volcanic ash drivers of ecosystem change in high mountain forests, British Columbia, Canada
Bibliographic record
Abstract
Northwest North America has unique high elevation Picea–Abies forests and parkland classified in British Columbia as the Engelmann Spruce–Subalpine–fir (ESSF) Biogeoclimatic zone. These ecosystems occur on a topographically and climatically complex landscape, juxtaposed with diverse vegetation types including alpine tundra, inland rain forest, dry conifer forest, and grasslands. Spatio-temporal ecosystem disturbance is varied, driven by factors such as climate variation, wildfire, volcanic eruptions, and insect herbivory. A pollen and charcoal record derived from a lake sediment core from the ESSF reveals a unique late-glacial to modern vegetation history progressing from alpine steppe through dry open conifer forest to moist spruce–fir ecosystems, the latter arising only 4600 years ago, late by comparison to other ESSF sites in the region. Repeated disturbance in the mid Holocene by wildfire coupled with volcanic ash deposition and increased climatic variation resulted in recurring Pinus contorta-dominated seral forest stands before cooling and moistening in the late Holocene led to stable Picea–Abies forest. With rapid climate change, changing disturbance regimes, and timber harvest, the management of dry ESSF forests needs to consider that this forest-type could transform into parkland or open seral pine stands, with a high frequency disturbance regime.
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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 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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".