Alien species established in the Bay of Biscay suggest significant contribution of both fouling and dry ship compartments
Bibliographic record
Abstract
Introduction The majority of studies on marine biosecurity focus on aquatic species, but invaders can also use dry ship compartments for dispersal; this pathway has been less explored than fouling or ballast water. Here we analysed the maritime traffic of the international Port of Gijon (south Bay of Biscay, Spain) as a case study. Methods Main donor countries were identified from traffic data. From the Global Invasive Species Database, we detected a similar proportion of expected global alien invasive species (AIS) from fouling and containers pathways to be actually settled in the recipient region. Results The AIS diversity transported via fouling was higher than that transported by containers. No significant difference between Australia, Brazil, Canada and South Africa in the proportion of global AIS actually transferred to Bay of Biscay supports the vision of port networks as a new ecosystem with multiple demes. Different AIS catalogues in the donor regions may explain a higher proportion of terrestrial AIS introduced via containers from Brazil and South Africa and marine AIS introduced via fouling. Discussion A more careful surveillance of biota in dry ship compartments, including it in a future revision in the International Convention for Safe Containers, is recommended to reduce the contribution of shipping to biological invasions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".