A decision support tool to prioritize ballast water compliance monitoring by ranking risk of non‐indigenous species establishment
Bibliographic record
Abstract
Abstract Despite the availability of research which has direct applications to environmental management, there is often a disconnect between scientific research and applied management that presents challenges for using academic knowledge in day‐to‐day operations by non‐scientists. A science‐based decision support tool was developed in partnership with Canada's marine authority, Transport Canada, for use by ballast water inspectors in their daily operations to inform prioritization of ships for regulatory compliance inspections. This science‐based tool combines information on the two primary pathway‐level predictors of species establishment success: environmental matching between source and recipient locations and propagule pressure (introduction effort), to generate risk estimates and relative rankings using data taken directly from ballast water reporting forms submitted by arriving ships. This tool thus packages the best available scientific knowledge in such a way as to be readily accessible for day‐to‐day decision‐making. While this tool was developed for Canada, it could be applied in any country with very little, if any, modification. This tool can also be updated in the future to incorporate advances in scientific understanding of ballast‐mediated introductions of non‐indigenous species. Synthesis and applications. Partnerships between scientists and managers are essential for ensuring that best‐available science translates into effective adaptive management. Recognizing a need to inform ballast water management compliance inspections, a tool was created that automatically estimates relative risk of establishment of non‐indigenous species for arriving ships. This information can be used by ballast water inspectors developing priorities for resource‐limited regulatory compliance inspections.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".