Using simulation to understand annual sea lamprey marking rates on lake trout
Bibliographic record
Abstract
Sea lampreys attack fish, killing some and leaving marks on others. Great Lakes fishery managers rely on observed marking rates to assess the success of the sea lamprey control program and estimate sea lamprey-induced mortality of lake trout. Because marking rates are only observed on survivors of sea lamprey attacks, they may not provide a reliable index of actual attack or mortality rates. To investigate the effect of survivor bias, we developed a simulation model representing a single season (June–December) of sea lamprey attacks. Simulated attack rates varied with month and lake trout size; simulated pierce and lethality rates varied with month alone. Surveyed marking rates were represented by simulated survivors in October; true rates were calculated from all simulated lake trout (dead and alive) in December. Simulation results were subsetted to include only those within the range of marking rates actually observed in the Great Lakes. Type A (piercing) marking rates were a good index of the sea lamprey attack rate and the sea lamprey-induced mortality rate if annual lethality rates were relatively constant. Type B (non-piercing) marking rates were a good index of the sea lamprey attack rate and the sea lamprey-induced mortality rate if annual pierce rates were relatively constant. Due to the uncertainty surrounding the pierce and lethality rates, we recommend that sea lamprey abundance information be incorporated in existing lake trout statistical catch-at-age models via a functional response component relating sea lamprey feeding to lake trout abundance, if possible.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".