Effects of female attributes and egg properties on offspring viability in a rheophilic cyprinid, <i>Chondrostoma nasus</i>
Bibliographic record
Abstract
Intraspecific differences in female attributes influence egg quality and, as a consequence, offspring viability. To further investigate this hypothesis, we compared female attributes, egg size, biochemical egg composition, and survival potential of offspring from 20 female spawners of an endangered rheophilic cyprinid, Chondrostoma nasus. Egg size was strongly related to female age and size. The chemical composition of egg dry matter was influenced by female size to a lesser extent. No significant relationship between egg size and egg dry matter composition was observed. Mortality curves revealed three distinct periods of elevated mortality: early mortality, hatching mortality, and starvation mortality, separated by periods of reduced mortality. No significant correlations between embryonic mortality (early mortality and hatching mortality) and egg size were found. Starvation mortality was size selective: resistance to starvation correlated significantly with egg size and egg energy content. The direct and indirect relationhips between female attributes, egg quality, and offspring viability show that the two main components of offspring viability (embryonic mortality and larval resistance to starvation) are not interrelated and that the sequence female attributes - egg size - larval resistance to starvation is the main pathway along which size selectivity operates.
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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 | 0.000 | 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".