Can trans‐generational experiments be used to enhance species resilience to ocean warming and acidification?
Bibliographic record
Abstract
Abstract Human‐assisted, trans‐generational exposure to ocean warming and acidification has been proposed as a conservation and/or restoration tool to produce resilient offspring. To improve our understanding of the need for and the efficacy of this approach, we characterized life‐history and physiological responses in offspring of the marine polychaete Ophryotrocha labronica exposed to predicted ocean warming (OW: + 3°C), ocean acidification (OA: pH −0.5) and their combination (OWA: + 3°C, pH −0.5), following the exposure of their parents to either control conditions (within‐generational exposure) or the same conditions (trans‐generational exposure). Trans‐generational exposure to OW fully alleviated the negative effects of within‐generational exposure to OW on fecundity and egg volume and was accompanied by increased metabolic activity. While within‐generational exposure to OA reduced juvenile growth rates and egg volume, trans‐generational exposure alleviated the former but could not restore the latter. Surprisingly, exposure to OWA had no negative impacts within‐ or trans‐generationally. Our results highlight the potential for trans‐generational laboratory experiments in producing offspring that are resilient to OW and OA. However, trans‐generational exposure does not always appear to improve traits and therefore may not be a universally useful tool for all species in the face of global change.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".