Winter severity, fish community, and availability to traps explain most of the variability in estimates of adult sea lamprey in Lake Superior
Bibliographic record
Abstract
Animal populations are assessed to estimate rates of artificial and natural mortality at ecologically relevant spatial and temporal scales to develop exploitation quotas. But how the population’s natural mortality rate and how the ability to observe the population changes through time are poorly understood in most invasive fishes, despite efforts to control their populations. By investigating a 30-year abundance index of invasive sea lamprey (Petromyzon marinus) in Lake Superior, we found that the index was highly correlated (R2 = 0.75) with biotic and abiotic factors hypothesized to influence sea lamprey natural mortality and their availability to index traps. The index was lowest in years (1) following winters with above average ice cover on Lake Superior, (2) when stream discharge during sea lamprey migration was below average, (3) when adult sea lamprey were smaller than average, and (4) when adult sea lamprey were more likely to be distributed in tributaries on the east side of Lake Superior. These results highlight the need for policy makers to consider invasive species abundance indexes not just in the context of control effort, but also in the context of biotic and abiotic conditions because they could markedly influence natural mortality or the ability to observe highly suppressed populations.
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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 | 0.001 |
| 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".