Use of physiological knowledge to control the invasive sea lamprey (Petromyzon marinus) in the Laurentian Great Lakes
Bibliographic record
Abstract
) control in the Laurentian Great Lakes of North America is an example of using physiological knowledge to successfully control an invasive species and rehabilitate an ecosystem and valuable fishery. The parasitic sea lamprey contributed to the devastating collapse of native fish communities after invading the Great Lakes during the 1800s and early 1900s. Economic tragedy ensued with the loss of the fishery and severe impacts to property values and tourism resulting from sea lamprey-induced ecological changes. To control the sea lamprey and rehabilitate the once vibrant Great Lakes ecosystem and economy, the Great Lakes Fishery Commission (Commission) was formed by treaty between Canada and the United States in 1955. The Commission has developed a sea lamprey control programme based on their physiological vulnerabilities, which includes (i) the application of selective pesticides (lampricides), which successfully kill sedentary sea lamprey larvae in their natal streams; (ii) barriers to spawning migrations and associated traps to prevent infestations of upstream habitats and remove adult sea lamprey before they reproduce; and (iii) the release of sterilized males to reduce the reproductive potential of spawning populations in select streams. Since 1958, the application of the sea lamprey control programme has suppressed sea lamprey populations by ~90% from peak abundance. Great Lakes fish populations have rebounded and the economy is now thriving. In hopes of further enhancing the efficacy and selectivity of the sea lamprey control programme, the Commission is exploring the use of (i) sea lamprey chemosensory cues (pheromones and alarm cues) to manipulate behaviours and physiologies, and (ii) genetics to identify and manipulate genes associated with key physiological functions, for control purposes. Overall, the Commission capitalizes on the unique physiology of the sea lamprey and strives to develop a diverse integrated programme to successfully control a once devastating invasive species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".