A multi-century history of fire regimes along a transect of mixed-conifer forests in central Oregon, U.S.A.
Bibliographic record
Abstract
Dry mixed-conifer forests are widespread in the interior Pacific Northwest, but their historical fire regimes are poorly characterized, in particular the relative mix of low- and high-severity fire. We reconstructed a multi-century history of fire from tree rings in dry mixed-conifer forests in central Oregon. These forests are dominated by ponderosa pine (Pinus ponderosa Lawson & C. Lawson), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), and grand fir (Abies grandis (Douglas ex D. Don) Lindl.). Across four, 30-plot grids of ∼800 ha covering a mosaic of dry mixed-conifer forest types, we sampled 4065 trees for evidence of both high- and low-severity fire. From 1650 to ∼1900, all four sites sustained frequent, often extensive, low-severity fires that sometimes included small patches of severe fire (50–150 ha during 18%–28% of fire years). Fire intervals were similar among sites and also among forest types within sites (mean intervals of 14–32 years). To characterize the continuous nature of the variation in fire severity, we computed a plot-based index that captures the relative occurrence of low- and high-severity fire. Our work contributes to the growing understanding of variation in past fire regimes in the complex and dynamic forests of North America’s Interior West.
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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 | 0.001 | 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".