A management-scale investigation of consistent individual differences in behaviour and trapping bias in sea lamprey (<i>Petromyzon marinus</i>)
Bibliographic record
Abstract
The sea lamprey (Petromyzon marinus) is invasive in the Laurentian Great Lakes. Trapping in large rivers could suppress sea lamprey recruitment by removing migrating adults prior to spawning. Currently, the proportion of sea lamprey trapped (efficiency) is too low for control purposes, possibly because trapping is biased toward certain behavioural types. We tested if individual differences in time to enter a novel environment (risk-taking) and proportion of time moving (activity) under standardized laboratory conditions were correlated with time to encounter and enter a trap in the field. 638 sea lamprey were tagged, assessed for risk-taking and activity in sequential trials, and released in the river to be trapped. In the laboratory, individuals differed consistently in risk-taking and activity behaviours, and more active individuals entered a simulated trap sooner than less active individuals. In the field, however, the times to first trap encounter, and capture in a trap, were not correlated with risk-taking or activity. Our study provides a novel demonstration of how patterns from small-scale behavioural studies may not extend to management-scale applications.
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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 | 0.000 | 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".