How have wildfires affected forest basal area in Northern and Southern California during the 21st century?
Bibliographic record
Abstract
Wildfires are unplanned and dynamic fires that occur in areas of combustible vegetation. They can be natural or human-induced and play a vital role in ecosystem health. The severity and intensity of wildfires can change over time based on the weather, available fuel and topography. California is continuously experiencing longer wildfire seasons as a direct result of climate change, affecting the forest structure of the state’s forest. Forest basal area is used to determine forest stand density and is an indicator of annual growth potential (Nix, 2020). It is often used as the basis for making important forest management decisions. My research will determine how wildfire frequency and extent have affected the basal area of Northern and Southern California’s forests in 2000 and 2017. Fire perimeter and frequency data obtained from the United States Forest Service regional datasets website will be used to define fire characteristics in the regions. Tree basal area data obtained from the United States Forest Service regional-level datasets website will be used to quantify the basal area of critical forest types. Percent change in basal area, fire frequency and acres burned each year will be determined to compare the burn regimes of the two regions during the two years to see how that has impacted the forest’s basal area. The findings will give an idea of how the rate of wildfires has changed in the last two decades and its subsequent impacts, which can help us better prepare for the future of forests in an ongoing climate crisis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".