Modeling the Impacts of Pest Damage
Bibliographic record
Abstract
A pest, by definition, is an organism that causes harm to mankind, crops, or property. Therefore, the concepts of pest and damage are closely linked. These are usually, although not exclusively, caused by insects and pathogens. Forested lands are usually managed on a multiple-use basis, that is, for the simultaneous production of several resources from the same unit of land. Resources include timber products and ecosystem services such as water for irrigation or human consumption, livestock and wildlife forage, wildlife protection, recreation, and fish habitat. Consequently, in multiple-use forestry, pest damage can be defined as the negative effects of an organism on the quantity and quality of the entire array of resources that are expected from a unit of land. The role of the silviculturist is to create a forest that will produce resources of the desired kind, in the right amount, and of the right value while maximizing all timber and nontimber values and minimizing the impacts on ecosystem function. This chapter reviews (1) the requirements that need to be considered when developing models that simulate tree and pest interactions, (2) evaluates pest damage to timber resources, and (3) describes case studies based on a suite of models that assist forest managers in BC, Canada, to determine growth and yield and interact with pest damage. This chapter underscores how pests affect forest management decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".