Connecting Island Communities on a Global Scale: Case Studies in Island Biosecurity
Bibliographic record
Abstract
Invasive alien species represent one of the greatest threats to island ecosystems and the unique species that inhabit them. In many instances, eradication or control programs for invasive alien species have effectively curtailed the ongoing loss of biodiversity on islands. Prevention is a more proactive and cost-effective approach, however, and is an emerging global priority in the conservation of island ecosystems. Island biosecurity programs attempt to prevent the introduction and establishment of invasive alien species on islands and dictate actions when an invasive species is detected. Targeted and robust collaboration efforts among the global island community on biosecurity advances and challenges can strengthen and improve local biosecurity programs. In this paper we review the principal tenets of island biosecurity—prevention, detection, and response—using case studies of current island biosecurity programs from New Zealand, Chile, Mexico, the United States, and Canada. Systematic evaluations of biosecurity activities are necessary to ensure that programs are effective and relevant. Key priority actions for the future include strengthening global collaboration on biosecurity through holding annual meetings, sharing resources online, leveraging funding opportunities, and forming working groups that will be engaged in improving critically important but under-resourced biosecurity programs.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| 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".