Next-generation lampricides: a three-stage process to develop improved control tools for invasive sea lamprey
Bibliographic record
Abstract
Successful integrated management of the invasive predatory sea lamprey (Petromyzon marinus) in the Laurentian Great Lakes of North America is owed largely to the long history of beneficial use of two lampricides: 3-trifluoromethyl-4-nitrophenol (TFM) and 2′,5-dichloro-4′-nitrosalicylanilide (niclosamide). Ensuring continued successful sea lamprey control necessitates consideration of possible next-generation lampricides to supplement or replace current lampricides. This review identifies fifteen hallmarks of success for current lampricides to be used as design criteria in a search for next-generation lampricides. A three-stage research approach is outlined. Targeted research using omics, computer modelling, and high-throughput technology to define molecular mechanisms and high probability molecular targets for sea lamprey selective toxic action is crucial to prioritizing chemical candidates. Targeted delivery or identifying synergists to existing or new lampricides can provide increased efficiency and reduced environmental impact. Ultimate development of next-generation lampricides will rely on traditional toxicity testing methodologies to ensure safety and regulatory compliance.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".