Within season short-term hatching delays suggest risk-spreading behaviour in populations of the freshwater cladoceran <i>Daphnia</i>
Bibliographic record
Abstract
Many organisms produce dormant stages, building up large germ banks in the sediments. Timing of emergence from these germ banks is a key feature in the recurrent cycle of dormant and active life stages. We exposed dormant egg bank samples of Daphnia magna from (semi-)permanent ponds to seasonal cues mimicking temperature and photoperiod during early spring, late spring, and summer in the region of sampling and investigated hatching success and response time after incubation. Whereas no differences in hatching success were observed between seasonal treatments, within each seasonal treatment a variable fraction of eggs postponed the hatching response for several days. We link immediate versus short-term delayed hatching to a trade-off between the advantages of quick niche filling and the unpredictability of reproductive success under unstable environmental conditions. A scenario where hatching investment is spread over immediate and delayed hatchers as a bet-hedging strategy is corroborated by environmental data on the risk of abortive hatching in early spring and by modeling results that show that immediate versus short-term delayed hatching is a valid alternative to bet-hedging over years in the investigated habitats to cope with variability in the onset of favourable conditions.
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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".