Predation on winter flounder (<i>Pseudopleuronectes americanus</i>) eggs by the sand shrimp (<i>Crangon septemspinosa</i>)
Bibliographic record
Abstract
This study estimated rates of sand shrimp (Crangon septemspinosa) predation on winter flounder (Pseudopleuronectes americanus) eggs and examined the effect of temperature on density-dependent mortality of early-stage flounder. In laboratory experiments, shrimp feeding rates on flounder eggs were positively correlated with temperature and shrimp size. Immunological assays of shrimp stomach contents indicated that 7.2% of shrimp collected from the Niantic River (Connecticut) had flounder eggs in their stomachs. Incidence of egg predation was highest in February (20%) and decreased continuously into early April (1.2%). In a deterministic model simulating predator-induced mortality of flounder eggs during a spawning season, shrimp consumed 0.4%49.7% of the total flounder spawn. Variations in shrimp population abundance and size structure accounted for the greatest variability in egg mortality. Water temperature during the spawning season presumably alters the population dynamics of early-stage flounder. In a long-term survey, the number of yolk-sac flounder larvae in warm years (≥4.3 °C) was depressed at high egg densities, indicating strong compensatory processes that increased egg mortality and limited the abundance of larvae. Failure of flounder to produce strong year-classes of larvae during warm years, possibly resulting from altered trophic dynamics, may explain the inability of stocks to recover from previous overexploitation.
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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 | 0.000 |
| 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 | 0.000 | 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 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".