Fire history of a naturally fragmented landscape in central Oregon
Bibliographic record
Abstract
We examined the fire history of 11 forest isolates surrounded by lava flows (kipukas) in central Oregon to determine historical differences in fire regimes between kipukas and the surrounding forest, and the role of spatial and environmental variables in fire occurrence. Tree-ring analysis and statistical comparisons show that historical agency records underestimate the number of fires based on the incidence of fire scars. Fires occurred more frequently on kipukas, were typically smaller, and were predominantly lightning-initiated. Except for three widespread fires, fires on kipukas and in the surrounding forest were largely asynchronous. The mean fire-return interval (MFRI) in the surrounding forest decreased following Euro-American settlement and increased on the kipukas with spot-fire removal. This suggests either that forest management and fire exclusion in the surrounding forest decreased fire spread to the kipukas, or that most fires originated on the kipukas. MFRI correlates strongly with distance to the nearest kipuka and a distance-weighted isolation index. The number of fires correlates with elevation change and distance to the nearest kipuka. Fire in naturally fragmented landscapes is influenced by the spatial arrangement of patches, environmental conditions, and human activities. Reconstructing fire histories from forest isolates in the context of their mainland counterparts may have methodological advantages and theoretical implications for forested landscapes characterized by human-imposed insularity.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".