Evidence of a historical frequent, low-severity fire regime in western Washington, USA
Bibliographic record
Abstract
Fire is a common disturbance in many forests. We conducted a fire history study on 40 Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) trees from two sites, Kellett Bluff and Turn Point, in the San Juan Islands of Washington state, USA. In total, 146 fire scars were identified and dated, representing 34–35 fires per site. Fires occurred between 1565 and 1964. Individual trees recorded up to 10 fires. The composite mean fire interval (MFI) was 11–13 years over the entire study period and 6 years in the historical period (1780–1895). These sites were structured by frequent, low-severity fires, yet supported a tree component for centuries — the oldest tree in this study was more than 500 years old. A program of frequent, low-severity fires may be critical for their long-term persistence. Comparisons of fire history data among these and five other local sites indicate frequent fires but little synchronicity; the MFI was 4 years, but most fires were only recorded at single sites. Although forests west of the Cascade Mountains are often described as subject to infrequent, high-severity fires, these results highlight the need for a more refined understanding of historical fire regimes in this region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".