Reproducing age variability in grass carp egg samples from the lower Sandusky River, Ohio, USA, using an egg-drift model
Bibliographic record
Abstract
Invasive grass carp (Ctenopharyngodon idella) are currently reproducing in several tributaries to Lake Erie and threatening the Great Lakes ecosystem and fisheries. Grass carp are pelagic river spawners whose fertilized eggs drift downstream from the spawning site, developing as they drift. Variability in spawning time and location together with nonuniform velocities in natural rivers leads to egg age variability in field samples at downstream sampling sites. In this study, the Fluvial Egg Drift Simulator (FluEgg) model was used to simulate the transport of grass carp eggs collected in 12 samples at 9 sites in the lower Sandusky River (Ohio, USA) on July 12, 2017, to replicate the observed variability in egg-age distributions present in field samples. The variability in egg ages in virtual samples compare well to field samples. The most plausible explanations for differences between virtual and field samples are the existence of multiple spawning locations, including a spawning area approximately 8 km upstream from the river mouth, and idealized flow fields derived from a one-dimensional hydraulic model. Despite multiple sources of uncertainty and the deficiency in prescribing detailed spawning activities in the simulations, the results validate the utility of FluEgg together with ichthyoplankton data to identify plausible spawning areas and interpret age variability in field samples. A comprehensive discussion of model limitations and ichthyoplankton sample interpretation provides guidance for those using drift models to inform management actions for control of invasive carp in North America and to protect and restore carp populations in their native range in Asia.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".