Mixed-severity fire in lodgepole pine dominated forests: are historical regimes sustainable on Oregon’s Pumice Plateau, USA?
Bibliographic record
Abstract
In parts of central Oregon, coarse-textured pumice substrates limit forest composition to low-density lodgepole pine (Pinus contorta Douglas ex Loudon var. latifolia Engelm. ex S. Watson) with scattered ponderosa pine (Pinus ponderosa Lawson & C. Lawson) and a shrub understory dominated by antelope bitterbrush (Purshia tridentata (Pursh) DC.). We reconstructed the historical fire regime from tree rings and simulated fire behavior over 783 ha of this forest type. For centuries (1650–1900), extensive mixed-severity fires occurred every 26 to 82 years, creating a multi-aged forest and shrub mosaic. Simulation modeling suggests that the historical mix of surface and passive crown fire were primarily driven by shrub biomass and wind speed. However, a century of fire exclusion has reduced the potential for the high-severity patches of fire that were common historically, likely by reducing bitterbrush cover, the primary ladder fuel. This reduced shrub cover is likely to persist until fire or insects create new canopy gaps. Crown fire potential may increase even with current fuel loadings if the climate predicted for midcentury lowers fuel moistures, but only under rare extreme winds. This study expands our emerging understanding of complexity in the disturbance dynamics of lodgepole pine across its broad North American range.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".