Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 2—Optimal river conditions for egg and larval drift
Bibliographic record
Abstract
This study uses a one-dimensional steady-state hydraulic model and the Fluvial Egg Drift Simulator (FluEgg) to model the drift and dispersion of grass carp eggs and larvae in the Maumee River, Ohio, for 180 scenarios representing different combinations of 10 river flows, 6 water temperatures, and 3 spawning locations. The FluEgg simulations were used to quantify in-river suspended hatching rates (the percentage of eggs that hatch within the river and in suspension) and in-river larval retention rates (the percentage of larvae that reach the gas bladder inflation stage within the river after hatching in suspension), and identify which scenarios produce the highest likelihood of recruitment. The simulations indicate that at low flows, in-river suspended hatching and larval retention rates in the Maumee River are limited by the capacity of the flow to keep fertilized eggs in suspension, whereas at high flows, the limiting factor is the distance available for the eggs/larvae to drift in the river. A wide range of scenarios result in eggs hatching within the river, but all larvae drift into Maumee Bay prior to the gas bladder inflation stage when flows exceed the mean annual flow. The simulations were assessed in the context of the hydraulic conditions that trigger spawning and maximize egg fertilization and the nursery habitat requirements for larval grass carp. The results indicate that the Maumee River, although suitable for grass carp spawning, may not be an ideal setting for recruitment unless Maumee Bay provides adequate nursery habitat for larvae.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".