Why does egg size of salmonids increase with the mean size of population spawning gravels?
Bibliographic record
Abstract
Population mean egg size of salmonids increases with the mean size of gravels in which a population spawns. A long-standing hypothesis is that large larvae cannot navigate small-gravel interstices, so mothers must decrease per-offspring investment when spawning gravels are small. We manipulated the size of incubation gravels and egg size of Atlantic salmon (Salmo salar) to test whether size-related entombment of larvae occurs. We find little evidence of size-related entombment, but we find evidence that gravel size does not affect all larval sizes equally. Larger larvae emerge from small gravels before development is complete and with a visible yolk sac, possibly due to oxygen limitation in small gravels. Smaller larvae always complete development in the gravel and emerge without a yolk sac. Although growth and survival may increase with juvenile size following yolk-sac absorption, juveniles with yolk sacs may fare worse when depredation rates are high. The egg size – gravel size correlation may therefore reflect increased postemergence mortality among larger offspring in small-gravel environments. Alternatively, compaction stress in fine gravels coupled with size asymmetries in larval strength may have caused the patterns that we observed, in which case our data may not help explain the egg size – gravel size correlation.
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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 | 0.003 |
| 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) | 0.002 | 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".