Where you trap matters: Implications for integrated sea lamprey management
Bibliographic record
Abstract
Barriers and pesticides have been used in streams to control sea lamprey in the Laurentian Great Lakes for nearly 70 years. Considerable effort has been spent to develop additional control measures, but much less effort has gone toward identifying how or where additional control measures might be cost-effectively integrated into the sea lamprey control program. We use a management strategy evaluation model in Lake Michigan to identify the stream types that would be most suitable for deploying traps to remove adults prior to spawning and estimate the likely impact on adult sea lamprey abundance in subsequent years under several trapping scenarios relative to status quo abundance. The greatest reduction in lake-wide adult sea lamprey abundance predicted by the model resulted when removing adult sea lampreys from streams that are difficult for control program personnel to treat with lampricide because lampricide applications would be required less frequently. Additionally, targeting streams which experience regular sea lamprey recruitment and streams with low adult sea lamprey density should result in reduced lake-wide abundance if trapping costs are relatively low or removal is high. Our results provide direction on where to trap and why, and indicate that trapping may be a valuable part of an integrated sea lamprey control approach advancing the goals of the Great Lakes Fishery Commission.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".