Dead giveaway: Rising mortality rates suggest effectiveness of Lake Erie grass carp (Ctenopharyngodon idella) response
Bibliographic record
Abstract
Grass carp ( Ctenopharyngodon idella ) are large, invasive fish that threaten Lake Erie’s economy and ecosystem. Incidental catches of grass carp have occurred since the 1980s in Lake Erie, while multi-day removal events were carried out in 2014 and 2017. To mitigate ecosystem impacts, a large-scale, multi-agency response to remove as many grass carp as possible from the Lake Erie basin (“strike teams”) began in 2018 and has increased every year. To date, total annual removals of fish has been the primary measure of progress; however, total annual removals do not indicate how efforts are affecting the grass carp population. Population vital rates, such as mortality rate, can indicate population demographic changes and may provide an alternative approach to measure how removals have impacted the grass carp population. We estimated annual mortality rates using 553 grass carp, representing 82.9 % of all grass carp removed in the Lake Erie basin, using a hierarchical catch-curve model and catch-at-age data from 2014 to 2022. Annual average mortality rates were initially low (4.3 %) and increased between 2017 and 2022 with the highest mortality (13.6 %) observed in 2021. Positive correlations between mortality and the number of fish harvested per year suggest that removals may be driving increases in the grass carp mortality rate. This increase in mortality rate shows promise for controlling the spread of grass carp within the Lake Erie ecosystem. This research supports the needs of fishery managers to better understand grass carp population dynamics and the adaptive management framework identified in the Lake Erie Grass Carp Adaptive Response Strategy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".