Methods to assess landscape-scale risk of bark beetle infestation to support forest management decisions
Bibliographic record
Abstract
The objective of our paper is to provide practitioners with suggestions on how to select appropriate methods for risk assessment of bark beetle infestations at the landscape scale in order to support their particular management decisions and to motivate researchers to refine novel risk assessment methods. Methods developed to assist and inform management decisions for risk assessment of bark beetle infestations at the landscape scale have been diverse, ranging from simple empirical correlations to complex systems models. These approaches have examined different bark beetle species, forest types and systems, and management questions, and they differ in spatial and temporal precision, the types of processes included, and the form of output. Bark beetle risk assessment methods, however, share a common theme: they aim to quantify expected levels of attack and loss due to beetles. By focusing on this commonality, we present a gradient in which methods can be classified and ranked, ranging from more structural, pattern-oriented methods to more functional, process-oriented methods. Our objective is to describe a framework for comparing methods in terms of how risk is represented and in terms of the complexity of application. To illustrate how diverse methods can be cast within a common frame of reference, we describe and provide brief examples of four types of methods that we have used in British Columbia, Canada, to examine landscape-scale risk of mountain pine beetle attack in lodgepole pine forests. We then provide some guidance on how to select an appropriate method for a given system and set of questions. The most appropriate method is the simplest one that can address the questions, minimize uncertainty, and inform the decision process in the required timeframe. It is important that researchers and practitioners can view bark beetle risk-assessment methods as a toolkit and select appropriate tools for a given task, as no single method is best for all situations.
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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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".