Forest pests and their management in the Anthropocene
Bibliographic record
Abstract
Forest managers are facing unprecedented challenges from rapid changes in forest pests. The core causes are changes in climate, land use, and global distributions of organisms. Due to invasions and range expansions by pests, and propagation of nonnative trees, managers are increasingly confronted with pest problems outside their range of experience. There is a need to adapt pest management practices more quickly and efficiently than is possible when managers work in isolation and mainly learn by trial and error. Here we identify general tactics for adaptation of forest pest management in the Anthropocene: growth and application of practical theory; improved biosecurity against future invasions; improved monitoring, prediction, and mitigation; increased sharing of knowledge among regions, countries, and continents; management plans that anticipate continuing change; improved assessment of costs, benefits, and risks of possible responses to new potential pests; assessment of system responses to pest management decisions so that subsequent decisions are increasingly better informed; and improved understanding of the couplings between forests, forest management, and socioeconomic systems. Examples of success in forest management can aid in other sectors (e.g., agriculture, pastoralism, fisheries, and water resources) that are similarly important to our environmental security and similarly challenged by global change.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".