A framework for aquatic invasive species surveillance site selection and prioritization in the US waters of the Laurentian Great Lakes
Bibliographic record
Abstract
Risk-based prioritization for early detection monitoring is of utmost importance to prevent and mitigate invasive species impacts. The Great Lakes Water Quality Agreement, a binational commitment between the United States and Canada to restore and protect the waters of the Laurentian Great Lakes, identifies aquatic invasive species (AIS) as one of ten priority issues (annexes) that must be addressed to ensure the chemical, physical, and biological integrity of the Great Lakes. The Agreement calls out the need for a comprehensive strategy for detecting and tracking new and potentially invasive species. Yet, with a surface water area of 95, 000 square miles (246, 049 square km) and shoreline length of 10, 210 miles (16, 431 km), the Great Lakes represent a daunting challenge for prioritizing where AIS surveillance activities should occur. Our goal was to develop a spatially-explicit and quantitative approach for identifying the highest risk sites for AIS introduction into the US waters of the Great Lakes based on the cumulative risk of new introductions (including range expansions) from a range of pathways and associated taxa. We estimate "invasion risk" scores for nearly 6,000 sites (9 km x 9 km) across the Great Lakes basin using proxy measures for propagule pressure weighted by the proportion of taxa associated with each proxy variable. Proxy variables include human population, number of ship visits, marina size, number of ponds, and number of natural or artificial aquatic connections. In total, we identify more than 1,800 sites with invasion risk scores >0. A small subset of these 1,800+ sites accounts for a majority of predicted propagule pressure and are therefore logical targets for future surveillance and AIS prevention efforts. Many of the highest risk sites are located in western Lake Erie, southern Lake Michigan, and the St. Clair-Detroit River System.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".