Laboratory culture of<i>Dreissena polymorpha</i>larvae: spawning success, adult fecundity, and larval mortality patterns
Bibliographic record
Abstract
Understanding the entire life history, especially critical periods during the life cycle of a species, is important for understanding population dynamics and is crucial for control of nuisance species. Errors in estimates of growth rates and recruitment in field and modeling studies are likely to occur if one assumes uniform rather than size- or stage-specific mortalities for larval stages. Similarly, effects of brood-stock health on adult fecundity and larval mortality patterns might affect results of laboratory studies investigating larval life history. We maintained zebra mussels, Dreissena polymorpha (Pallas, 1771), in spawning condition in the laboratory for 24 weeks. Healthy larvae were produced for 18 weeks. However, induction of spawning and egg production declined through time. Fecundity increased with size per female, but sperm production did not for males. We found no correlation between clutch size and egg size across a wide range of clutch sizes. Survivorship was greater when larvae were reared at lower densities. Daily larval mortality was strongly size dependent; the average size of dead larvae was similar to the minimum size of live larvae. Highest daily larval mortality occurred during the transition from D stage to umbonal stage, supporting the suggestion of a developmental bottleneck as found in previous field studies.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".