The balance of complexity in mechanistic modeling: Risk analysis in the mountain pine beetle
Bibliographic record
Abstract
In most fields of applied ecology, there is a need to predict and manage the risk of pest outbreaks. One challenge to the development of mechanistic risk models is striking a balance between the tactical details of a system, and the strategic simplifications necessary to maintain generality and mathematical tractability. In this review we analyze the balance of complexity for risk models in the mountain pine beetle system. Mountain pine beetles are the single most destructive pine-forest pest in western North America. Much effort has gone into collecting empirical evidence and developing mechanistic outbreak models. Yet, current risk models only utilize tree-susceptibility indices that have proven ineffective at predicting the risk or extent of an infestation. We develop a conceptual framework of the beetle-host interaction that allows us to compare across both phenomenological and mechanistic models. From this framework, we demonstrate how current risk models emerged and why they predict ranked-risk as opposed to absoluterisk. Existing mechanistic models include a wide variety of possible interactions which has lead to disagreement about the ingredients essential for beetle outbreaks. By contrasting these models against the ecological framework, we extract systematic insight into the factors that determine risk, and suggest what dynamical processes must be modeled explicitly and what can be strategically abstracted.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".