Towards an integrated approach to modelling the risks and impacts of invasive forest species
Bibliographic record
Abstract
In this paper we provide an overview of an integrated approach to modelling the risks and impacts associated with non-indigenous forest pest species. This is a broad and important topic given the scale of ecological and economic consequences associated with non-indigenous species in North America and elsewhere. Assessments of risks and impacts remain difficult due to complexities and interactions between the many factors driving invasions and outcomes. These processes occur across various spatial and temporal scales, and are often influenced and complicated by human activities. For each component of an ecological invasion (i.e., arrival, establishment, and spread), we review general approaches for modelling the phenomenon and identify data and knowledge gaps. With the greater availability of various spatial data and computational power we suggest the possibility of linking the models for each invasion component into a more integrated framework, thus allowing interactions and feedbacks between components to be better incorporated into risk modelling efforts. The approach is illustrated using examples from current work with Sirex noctilio Fabricius — a relatively new invasive wood wasp in eastern North America.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".