International aquatic invasive species early detection efforts in the St. Clair-Detroit River System: A decadal review
Bibliographic record
Abstract
The St. Clair-Detroit River System (SCDRS) makes up the connecting channel between lakes Huron and Erie and contains a wide variety of habitats and fish diversity. However, given its large population centers and its utility for tourism, recreational fishing and boating, and as an international shipping channel, this connecting waterway is also one of the most high-risk areas for new aquatic invasive species (AIS) introductions in the Laurentian Great Lakes Basin. Fisheries and Oceans Canada and the U.S. Fish and Wildlife Service have been conducting AIS early detection efforts in this area since 2013 to find these newly introduced species before they establish and cause harm. Both programs have undergone numerous changes over the last decade to increase the likelihood a new species will be detected soon after introduction, including sampling new locations, increasing expended effort, and targeted sampling protocols. These changes have led to captures of several non-established species in the SCDRS, such as rudd ( Scardinius erythrophthalmus ), Atlantic salmon ( Salmo salar ), and coho salmon ( Oncorhynchus kisutch ), as well as increases in two different invasive species early detection analysis metrics. The purpose of this review is to describe the two early detection programs implemented in the SCDRS, their evolutions over the last decade, and the impact of these adaptations on program success.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".