Hurdles and opportunities in implementing marine biosecurity systems in data-poor regions
Bibliographic record
Abstract
Managing marine nonindigenous species (mNIS) is challenging, because marine environments are highly connected, allowing the dispersal of species across large spatial scales, including geopolitical borders. Cross-border inconsistencies in biosecurity management can promote the spread of mNIS across geopolitical borders, and incursions often go unnoticed or unreported. Collaborative surveillance programs can enhance the early detection of mNIS, when response may still be possible, and can foster capacity building around a common threat. Regional or international databases curated for mNIS can inform local monitoring programs and can foster real-time information exchange on mNIS of concern. When combined, local species reference libraries, publicly available mNIS databases, and predictive modeling can facilitate the development of biosecurity programs in regions lacking baseline data. Biosecurity programs should be practical, feasible, cost-effective, mainly focused on prevention and early detection, and be built on the collaboration and coordination of government, nongovernment organizations, stakeholders, and local citizens for a rapid response.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".