The ecology of forest insect invasions and advances in their management
Bibliographic record
Abstract
Invasions by nonindigenous forest insects can have spectacular effects on the biodiversity, ecology, and economy of affected areas. This introduction explores several critical issues that are generally relevant to invasions by forest insects to provide an extended background for this special issue of the Canadian Journal of Forest Research and highlights the key findings of the papers included in the issue. The topics covered address new information about (1) the role of cargo shipments as invasion pathways for the arrival of insects such as wood borers and bark beetles, (2) biogeographical effects that can influence the ecological and economic impact of insects feeding on exotic tree species, (3) the influence of biodiversity on impacts of forest insects and on the invasibility of ecosystem, and (4) recent advances in the detection, monitoring, and management of invasive species and native pests, including DNA barcoding for identification, the use of pheromones for monitoring and mating disruption, and biological control. These findings are likely to become even more important with elevated prevalence of invasions as a result of increasing global trade and international travel. Avenues of international communication and cooperation among scientists should be encouraged to enhance the sharing of information about biological invasions and to find solutions to this alarming problem.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".