A synthesized mating pheromone component increases adult sea lamprey (<i>Petromyzon marinus</i>) trap capture in management scenarios
Bibliographic record
Abstract
Application of chemical cues to manipulate adult sea lamprey (Petromyzon marinus) behavior is among the options considered for new sea lamprey control techniques in the Laurentian Great Lakes. A male mating pheromone component, 7α,12α,24-trihydroxy-3-one-5α-cholan-24-sulfate (3kPZS), lures ovulated female sea lamprey upstream into baited traps in experimental contexts with no odorant competition. A critical knowledge gap is whether this single pheromone component influences adult sea lamprey behavior in management contexts containing free-ranging sea lampreys. A solution of 3kPZS to reach a final in-stream concentration of 10−12 mol·L−1 was applied to eight Michigan streams at existing sea lamprey traps over 3 years, and catch rates were compared between paired 3kPZS-baited and unbaited traps. 3kPZS-baited traps captured significantly more sexually immature and mature sea lampreys, and overall yearly trapping efficiency within a stream averaged 10% higher during years when 3kPZS was applied. Video analysis of a trap funnel showed that the likelihood of sea lamprey trap entry after trap encounter was higher when the trap was 3kPZS baited. Our approach serves as a model for the development of similar control tools for sea lamprey and other aquatic invaders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".