A Temporal Assessment of Risk of Non-indigenous Species Introduction by Ballast Water to Canadian Coastal Waters Based on Environmental Similarity
Bibliographic record
Abstract
Abstract The environmental similarity scores between two locations are essential in ballast water risk assessment (BWRA) models used to estimate the potential for non-indigenous species introduction and guide management strategies aiming to minimize biodiversity loss and economic impacts. Previous BWRA models incorporate annual-scale environmental data, which may overlook seasonal variability. In this study, the differences in monthly sea surface temperature and salinity data were calculated at global ports and incorporated in a BWRA model. The environmental similarity scores were then calculated between the ballast water source and destination locations for ships arriving at Canadian coastal ports using monthly and annual-scale models for statistical comparison. As salinity and temperature vary seasonally in specific regions, there were significant differences in calculated environmental similarity risk values using annual and monthly-scale models. The results suggest that BWRA based on annual-scale data might underestimate environmental similarity scores. In contrast, methods incorporating monthly data can provide a more sensitive assessment to inform ballast water management practices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".