Trade-offs between suppression and eradication of sea lampreys from the Great Lakes
Bibliographic record
Abstract
Ecosystem managers confronted with newly invasive species may respond with a program of suppression or eradication. Suppression of an invasive species refers to management of a species such that its effect on other biota in the local ecosystem is acceptable. Eradication is the removal of all individuals of a species from a defined region. We examine the cost and benefit trade-offs between suppression and eradication of Laurentian Great Lakes sea lampreys (Petromyzon marinus) based on discussions at the 3rd Sea Lamprey International Symposium (held in 2019). Substantial effort has been expended annually since the 1960s to suppress sea lampreys in the Great Lakes basin. Choosing between suppression and eradication is a value judgement, ideally made jointly by scientists, decision-makers, stakeholders, and society. Successful large-scale eradications have been limited to a small number of cases for which the cost to human society justified and supported the long-term commitment necessary for success. The greatest challenge to successful eradication of sea lampreys from the Great Lakes may be a suitable social, political, legal, and institutional environment. Preparations could be made now for a transition in which public pushback on current control methods (pesticide applications and barriers to fish passage) leads to more extensive use of an alternative control method, such as genetic 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".