Management inferences from a demographic analysis of sea lamprey (<i>Petromyzon marinus</i>) in the Laurentian Great Lakes
Bibliographic record
Abstract
We use matrix models incorporating uncertainty in values of life history traits and density-dependent survival to assess pest management strategies implemented by the Great Lakes Fishery Commission to control nonnative sea lamprey (Petromyzon marinus) in the Laurentian Great Lakes. The primary method of sea lamprey control has been treatment of rearing tributaries with chemical lampricides, but release of sterilized males and deployment of migratory barriers and traps are important components of the management plan. Uncertainties in the effectiveness of alternative control remain, however. Our models demonstrated that the management target of reducing lampricide use by 20% while maintaining current levels of control could be achieved if alternative methods are used to suppress current lake-wide fecundity rates by 49%–65%, assuming equal lampricide efficiency on larvae and metamorphosing individuals, or by 42%–55% when lampricide mortality on larvae is assumed to be half of that on metamorphosing individuals. At current levels of lampricide use, reduction to 72%–88% of current fecundity rates is recommended to ensure long-term control of sea lamprey populations in the face of uncertainty in current estimates of population growth rates. New control options targeting additional vital rates, such as survival of the parasitic life stage, could further reduce reliance on lampricides while maintaining effective 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".