A 25-Year History of Spatial and Temporal Trends in Wildfire Activity in Oregon and Washington, U.S.A.
Bibliographic record
Abstract
We examined spatial and temporal trends in large fire incidents (> 400 ha) in Oregon and Washington. Our primary objectives were to examine whether patterns existed in the spatial and temporal distribution of large fires and to explore the relationship of patterns to climate variables using a geographic information system. We analyzed a historical fire database containing over 1000 fire incidents over a 25-year time period (1984 – 2008). We compared this fire database to climate variables representing historical estimates of average monthly maximum temperature, average monthly minimum temperate, average monthly dewpoint, and average monthly precipitation. Results showed an increasing trend in fire frequency, extent, magnitude, and fire season duration. Geospatial analytical techniques such as nearest neighbor analysis, quartic kernel density estimation, and quadrat analysis identified spatial patterning and hot spots of fire occurrence and size. The relationship of fire occurrence and size to climate variables was generally statistically indiscernible in our analysis results although some correlation was evident when fires were analyzed separately according to burn severity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".