Simulation of interactions among fire, mountain pine beetle and lodgepole pine forest
Bibliographic record
Abstract
This paper describes a modelling research approach for the proposed new study of the interaction of fire and mountain pine beetle via forest age structure. This approach is theoretical and provides an analysis of how the stability of forest age-distributions is related to fire regimes. Starting with the derivation of the theoretical negative exponential forest age-distribution, we have used three models to explore the conditions under which a stable age-distribution could be expected. The results suggested that a stable age-distribution could always be achieved as long as the forest age-specific mortality is constant over time, and the shape of a stable age-distribution is mainly determined by the forest age-specific mortality. However, the stability of the forest age-distribution will be reduced when a small variation in the age-specific mortality is introduced. The simulation results of the possible patterns of the age-distribution under various fire regimes indicated that a variety of agedistribution curves could appear, including negative exponential and also other curves with one or multiple peaks. The results suggested that a stable forest age-distribution might never be achieved if the forest landscape is subjected to large and irregular fire disturbances. The age distributions are then related to susceptibility to mountain pine beetle attack, via a susceptibility algorithm.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".