Experimental Analysis of Survival and Recovery of Ship Fouling Mussels During Transit Between Marine and Freshwaters
Bibliographic record
Abstract
Ships and boats may transport whole communities of non-indigenous species (NIS) through hull biofouling, some members of which may become invasive. Several studies have evaluated the diversity of these communities, but very few have analyzed the survival of organisms after their voyages into different and potentially inhospitable conditions. This factor is important to consider because the last port of call approach for risk assessments assumes that if the conditions observed in the last port of call are different from those observed in a receiving port, risks are diminished or null. Using an innovative experimental system, we tested the survival and recovery of the marine blue mussel (Mytilus edulis) and the freshwater zebra mussel (Dreissena polymorpha) by exposing them to adverse salinity conditions at varying temperatures to simulate ships and boats transiting to ports or marinas with contrasting environmental conditions. Both mussel species, which are well-known for their adaptability to new environments as aquatic NIS, survived better at colder temperatures, with blue mussels surviving up to 14 days in freshwater, and zebra mussels up to 8 days in marine water. This highlights the importance of considering the resistance of fouling organisms to adverse conditions in vector and species risk assessments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".