Alaska’s changing fire regime — implications for the vulnerability of its boreal forestsThis article is one of a selection of papers from The Dynamics of Change in Alaska’s Boreal Forests: Resilience and Vulnerability in Response to Climate Warming.
Bibliographic record
Abstract
A synthesis was carried out to examine Alaska’s boreal forest fire regime. During the 2000s, an average of 767 000 ha·year –1 burned, 50% higher than in any previous decade since the 1940s. Over the past 60 years, there was a decrease in the number of lightning-ignited fires, an increase in extreme lightning-ignited fire events, an increase in human-ignited fires, and a decrease in the number of extreme human-ignited fire events. The fraction of area burned from human-ignited fires fell from 26% for the 1950s and 1960s to 5% for the 1990s and 2000s, a result from the change in fire policy that gave the highest suppression priorities to fire events that occurred near human settlements. The amount of area burned during late-season fires increased over the past two decades. Deeper burning of surface organic layers in black spruce ( Picea mariana (Mill.) BSP) forests occurred during late-growing-season fires and on more well-drained sites. These trends all point to black spruce forests becoming increasingly vulnerable to the combined changes of key characteristics of Alaska’s fire regime, except on poorly drained sites, which are resistant to deep burning. The implications of these fire regime changes to the vulnerability and resilience of Alaska’s boreal forests and land and fire management are discussed.
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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 | 0.001 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".