Flammability of Dominant Tree Species Portends Severe Wildfire Risk in the Portland-Vancouver Metro Area
Bibliographic record
Abstract
Climate Change increases the occurrence of wildfire globally, largely through enhanced drying of plants which increases their flammability. However, these processes are poorly studied in less fire-prone ecosystems, such as the moist temperate coniferous forests of the Pacific Northwest. Likewise, flammability studies are rarely completed within urban forests. These knowledge gaps limit our ability to appreciate the true fire hazard within our cities and puts our communities at elevated risk of catastrophic wildfires. Recent research at Reed College quantified the shoot-level flammability and tissue moisture of 4 dominant tree species from moist Pacific Northwest forests (Pseudotsuga menziesii, Tsuga heterophylla, Thuja plicata, and Acer macrophyllum) and compared it to 4 tree species from more fire-prone dry forest / woodland types (Pinus ponderosa, Calocedrus decurrens, Sequioadendron giganteum, and Quercus garryana), using a custom-built flammability chamber. We found that our native moist forest species had higher flammability than dry forest species and that moisture status accurately predicts flammability. Leveraging satellite observations of fuel moisture allowed prediction of seasonal flammability of three local natural areas–Forest Park, Reed Canyon, and the Sandy River Gorge. Our model shows that the Sandy River Gorge reaches critical levels of flammability late in the growing season (August), while Reed Canyon reaches highly flammable levels a month earlier (July), and Forest Park remains highly flammable throughout the growing season. Our study provides important information on the timing of local wildfire hazard and reveals a potentially under-appreciated extreme latent fire risk within our largest urban natural park, Forest Park.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".