Bibliographic record
Abstract
To predict future wildfires, researchers are building models that better account for the vegetation that fans the flames . Wildfire ripped through the black spruce forests of Eagle Plains, Yukon Territory, Canada in 1990. Fire came again in 2005. By the time plant ecologist Carissa Brown arrived in the summer of 2007, all but a few trees were dead. Any seedlings that had sprouted after the first fire had burned in the second. Their charcoaled trunks had disintegrated by 2007, leaving open land furred with swishing grasses and tundra shrubs. “It’s not what you expect to find up there,” says Brown, at Memorial University of Newfoundland in St. John’s, Canada (1). Frequent fires dramatically change patches of the landscape in Eagle Plains, Yukon Territory, Canada. Consider the vegetation in the unburned black spruce forest ( Left ) compared with a stand burned about 100 years ago ( Middle ), compared with one burned in 1990 and 2005 ( Right ). Images credit: Carissa Brown (Memorial University of Newfoundland, St. John’s, NL, Canada). Yukon forests evolved to regenerate quickly after fire. Adult trees died in historical burns every 80 to 150 years, but the heat unsealed the burning trees’ small, resinous cones to drop their seeds, kick-starting the next generation. Seedlings that established in the first five to 10 years after a fire took decades to mature. But now, as the subarctic undergoes rapid warming, some hotter, drier forests are burning much more often, killing immature spruce trees before they have time to set cones. The problem is not limited to the far North. Around the world, wildfires are growing more frequent—as well as larger, hotter, and more destructive (2). Researchers can no longer look to the past as an accurate predictor of the future. Forests adapted to rare fires may not persist through frequent …
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.008 |
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".