Rapid evolution meets invasive species control: the potential for pesticide resistance in sea lamprey
Bibliographic record
Abstract
Rapid evolution of pest, pathogen, and wildlife populations can have undesirable effects, for example, when insects evolve resistance to pesticides or fishes evolve smaller body size in response to harvest. A destructive invasive species in the Laurentian Great Lakes, the sea lamprey (Petromyzon marinus) has been controlled with the pesticide 3-trifluoromethyl-4-nitrophenol (TFM) since the 1950s. We evaluated the likelihood of sea lamprey evolving resistance to TFM by (i) reviewing sea lamprey life history and control; (ii) identifying physiological and behavioural resistance strategies; (iii) estimating the strength of selection from TFM; (iv) assessing the timeline for evolution; and (v) analyzing historical toxicity data for evidence of resistance. The number of sea lamprey generations exposed to TFM was within the range observed for fish populations where rapid evolution has occurred. Mortality from TFM was estimated as 82%–90%, suggesting significant selective pressure. However, 57 years of toxicity data revealed no increase in lethal concentrations of TFM. Vigilance and the development of alternative controls are required to prevent this aquatic invasive species from evolving strategies to evade 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".