Fire-related landform associations of remnant old-growth trees in the southern Washington Cascade Range
Bibliographic record
Abstract
The spatial distribution of biological legacies left by natural disturbances is an important source of variability in forest development. We investigated one type of biological legacy: remnant old-growth trees persisting in mature Douglas-fir forests. We hypothesized that persistence varies with topographic heterogeneity influencing fire behavior. Our two study areas are located in the southern Washington Cascade Range, USA. They have an unfragmented, mature forest cover that regenerated following wildfire. We mapped all remnant old-growth trees (live and dead) within 4.26.4 km long belt transects. Digital elevation models were used to generate convergent and divergent landform classes. Frequency analysis was used to test for landform associations. Live remnant western hemlock and western redcedar were strongly associated with convergent landforms and aspects that had greater availability of soil moisture. Live remnant Douglas-fir were most abundant, but were not correlated with convergence or divergence, although certain landforms had higher concentrations. Remnant snags were abundant across convergent and divergent landforms. We conclude that species with low fire resistance survive most frequently on landforms that have a dampening effect on fire intensity. Topographic variability may indirectly influence ecological functions associated with biological legacies by affecting the spatial distributions of remnant old-growth trees.
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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".