The effect of turbulence on survival, dispersal, and swimming behavior of grass carp eggs and larvae
Bibliographic record
Abstract
There is an urgent need to monitor and control the spread of invasive grass carp (Ctenopharyngodon idella) in North America. Grass carp are reproducing in tributaries to Lake Erie and control efforts targeting reproduction are greatly needed. Current strategies for their control and removal are costly and report mixed degrees of efficacy. However, an alternative way to monitor and control their spread consists of increasing capture and mortality rates during early life stages (i.e. eggs and larvae stages) when they are more susceptible to damage via enhanced flow turbulence levels and altered flow conditions. In order for these alternative strategies to be effective, is necessary to study the physics underlying the movement of eggs and larvae in streamflow, and to quantify the turbulence thresholds that trigger those behavioral and physiological effects.\nThis study examines how early-life-stage grass carp interact with turbulent flows, how turbulence affects their survival, and whether turbulence-based control methods could work. An extensive series of laboratory experiments were conducted with live grass carp eggs and larvae in a grid-stirred turbulence tank and in a race-track flume to: a) explore the effect of turbulence intensity on egg mortality, and b) to document the behavioral response of grass carp larvae to spatially-variable, turbulent flows. \nA turbulence intensity threshold was identified, above which egg mortality substantially increased due to short- (10 seconds) and long-term (5 minutes) exposure at different turbulence levels. Larvae actively responded to changes in turbulence intensity and shear stresses produced by obstructions in the flow (e.g. rocks, piers, and submerged vegetation), avoiding areas of high shear and seeking low-turbulence, low-vorticity regions. These swimming capabilities were quantified by estimating burst swimming speeds and were correlated with the spatial distributions of turbulent kinetic energy, vorticity, and Reynolds stresses for future predictions of larvae dispersion on natural streams.\nThis study produced a unique and extensive data set that may allow for the development of turbulence-based control methods for grass carp. Such control methods could include increasing egg mortality by increasing turbulence intensity through temporary and permanent in-stream structures or using natural or modified hydrodynamics to attract, guide, and aggregate larvae at predefined control points for collection or extermination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 teacher head, 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".