Capturing potential: Leveraging grass carp behavior Ctenopharyngodon idella for enhanced removal
Bibliographic record
Abstract
Effective management of invasive species benefits from a comprehensive understanding of the species’ behavior and interactions with the invaded system. We investigated temporal dynamics of telemetry detections and the potential utility of a traitor approach for informing response efforts to the invasive grass carp (Ctenopharyngodon idella) population in the Sandusky River, a major tributary to Lake Erie. Telemetered grass carp exhibited heightened activity at night and early morning, suggesting that capture and removal be more effective during these time periods. Analysis of catch per unit effort (CPUE) across different removal methods, trammel nets, electrofishing, and hoop nets. suggested that incorporating the traitor approach could improve capture. Low catchability values (<0.026), based on the number of telemetered grass carp present in the river on a weekly basis and the number of those telemetered fish captured, suggest the species is difficult to capture. Optimizing response effort efficiency is important and refining catchability estimates will lessen errors in population models and improve interpretation of low CPUE data. Results from generalized additive models suggest capture could be improved using telemetry data, night removals, and by attempting exploratory removal efforts in fall and winter months. By incorporating telemetry data and acknowledging the complexities of grass carp behavior and ecology, we found that a multifaceted and data-driven approach to invasive species control could be beneficial, ultimately promoting conservation and sustainability in dynamic ecosystems like Lake Erie.
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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 | 0.001 |
| 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 | 0.001 |
| 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".