New estimates of lethality of sea lamprey (<i>Petromyzon marinus</i>) attacks on lake trout (<i>Salvelinus namaycush</i>): implications for fisheries management
Bibliographic record
Abstract
Sea lamprey (Petromyzon marinus) control in North America costs millions of dollars each year, and control measures are guided by assessment of lamprey-induced damage to fisheries. The favored prey of sea lamprey in freshwater ecosystems has been lake trout (Salvelinus namaycush). A key parameter in assessing sea lamprey damage, as well as managing lake trout fisheries, is the probability of an adult lake trout surviving a lamprey attack. The conventional value for this parameter has been 0.55, based on laboratory experiments. In contrast, based on catch curve analysis, mark–recapture techniques, and observed wounding rates, we estimated that adult lake trout in Lake Champlain have a 0.74 probability of surviving a lamprey attack. Although sea lamprey growth in Lake Champlain was lower than that observed in Lake Huron, application of an individual-based model to both lakes indicated that the probability of surviving an attack in Lake Champlain was only 1.1 times higher than that in Lake Huron. Thus, we estimated that lake trout survive a lamprey attack in Lake Huron with a probability of 0.66. Therefore, our results suggested that lethality of a sea lamprey attack on lake trout has been overestimated in previous model applications used in fisheries management.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".