A review of sea lamprey control in Lake Erie, 2000–2019
Bibliographic record
Abstract
From 2000 to 2019, sea lamprey control in Lake Erie focused primarily on deploying lampricides and completing important barrier and dam renovation projects in tributaries including the Big Creek barrier, and Harpersfield and Dunnville dams. The number of Lake Erie stream treatments doubled during 2000–2019 compared to 1986–1999 due to changes in larval assessment and stream selection protocols. Despite increased lampricide use and few infested tributaries relative to other Great Lakes, achieving program targets for Lake Erie has been challenging, but progress has been made. The St. Clair River is a potential significant source of sea lampreys to Lake Erie harboring an estimated 920,000 larvae; however, to date, no lampricide treatments have occurred. In 1986, sea lamprey control began in Lake Erie primarily to restore lake trout. Prior to control efforts, sea lamprey induced mortality on lake trout was too high to allow the establishment of adult lake trout stocks necessary to achieve fishery program goals. Sea lamprey marks on adult lake trout declined dramatically in Lake Erie following sea lamprey control. State and provincial agencies have prioritized stocking the Seneca strain of lake trout, thought to be more resistant to sea lamprey attacks. This change in stocking strategy, coupled with more aggressive sea lamprey control, has contributed to increasing adult lake trout survival. Protecting species at risk in Lake Erie adds to the challenges faced by the sea lamprey control program, but productive collaboration has proven an effective strategy in dealing with the increasing complexity of sea lamprey control.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".