Spatial simulation of historical landscape patterns in coastal forests of the Pacific Northwest
Bibliographic record
Abstract
Concerns about the fragmentation of Pacific Northwest forests are based on the assumption that these landscapes historically contained large, contiguous patches of old growth. However, this supposition appears to conflict with disturbance history research, which shows that wildfire was an important component of pre-settlement forest ecosystems. To better quantify historical forest patterns, a spatial simulation model of wildfire and forest succession was used to simulate pre-settlement landscape dynamics in the Oregon Coast Range, U.S.A. The model was parameterized to simulate fire regimes over 1000 years prior to Euro-American settlement using data from paleoecological, dendro ecological, and historical sources. A simple fire-spread algorithm produced mosaics of variable fire severity and allowed simulated fires to be calibrated to match the shapes of real fires. The simulated landscape was spatially heterogeneous and highly dynamic. Old growth was the dominant patch type occupying a median of 42% of the total area. The relatively long fire return intervals, highly skewed fire size distributions, and mixed severities characteristic of the historical fire regime generated a landscape mosaic with large (> 100 000 ha) patches of old-growth forest, although smaller patches (<100 ha) were the most numerically abundant. Both small and large patches of old forest have important ecological roles in a dynamic ecosystem, and future landscape management efforts should consider the implications of altering these historical patterns.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".