Effects of Trap Funnel and Finger Design on Sea Lamprey Entrance and Retention
Bibliographic record
Abstract
Traps are used to catch adult sea lampreys during their upstream migration to estimate their abundance in streams and, in turn, provide a measure of the Sea Lamprey Control Program’s effectiveness. During 2015 and 2016, we experimentally compared two components of sea lamprey trap design: trap entrance funnel type and the presence of retention devices, using side-by-side instream test chambers as well as laboratory flumes. We modeled how likelihoods of entrance and retention were influenced by funnel type, retention fingers, water temperature, and lamprey sex. Likelihood of entrance was highest with bottom-oriented funnels and no retention fingers. As water temperature increased, the likelihood of entrance generally increased, but funnel type and retention fingers determined the magnitude of the increase. Likelihood of retention was highest with bottom-oriented funnels and retention fingers and was also influenced by water temperature. Overall, the likelihood of capture (result of entrance + retention) was highest for bottom-oriented funnels and varied by water temperature and lamprey sex but not retention fingers. Further testing on other components of trap design is needed. This type of controlled experimental design can help guide future work to improve trap exploitation rates.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".