Metabolic depression explains differences in vulnerability to low oxygen between two species of temperate marine bivalves
Bibliographic record
Abstract
). On day 25, when all bivalves in the anoxic treatments had perished, the normoxic and hypoxic treatments were switched to anoxia to further explore the long-term effects of prior hypoxia on survival. Respiration rate could not be carried out under anoxia, and feeding was negligible; accordingly, detailed analyses were limited to the comparison of normoxia vs. hypoxia. Both species reduced pumping rate under hypoxia and further suppressed it to negligible levels under anoxia; however, only oysters reduced respiration rate, indicating metabolic depression. Oysters survived longer than mussels in all treatments. Moreover, oysters pre-exposed to hypoxia survived anoxia as long as normoxic controls, suggesting the physiological strategy adopted by oysters during hypoxia did not have major long-term effects on survival when further exposed to anoxia. Overall, these findings suggest that metabolic depression, rather than feeding reduction, underpins superior low-oxygen tolerance of C. virginica relative to M. edulis, implying that eastern oysters will better withstand low-oxygen episodes expected in eutrophic coastal waters under climate change scenarios. As the frequency and duration of low-oxygen events will increase in a warmer ocean, we provide preliminary support that species that can effectively engage metabolic depression under periods of high temperature and low-oxygen conditions may be more resilient to climate 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 | 0.000 | 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".