Hypoxia and High Temperature as Interacting Stressors: Will Plasticity Promote Resilience of Fishes in a Changing World?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AbstractDetermining the resilience of a species or population to climate change stressors is an important but difficult task because resilience can be affected both by genetically based variation and by various types of phenotypic plasticity. In addition, most of what is known about organismal responses is for single stressors in isolation, but environmental change involves multiple environmental factors acting in combination. Here, our goal is to summarize what is known about phenotypic plasticity in fishes in response to high temperature and low oxygen (hypoxia) in combination across multiple timescales, to ask how much resilience plasticity may provide in the face of climate change. There are relatively few studies investigating plasticity in response to these environmental stressors in combination; but the available data suggest that although fish have some capacity to adjust their phenotype and compensate for the negative effects of acute exposure to high temperature and hypoxia through acclimation or developmental plasticity, compensation is generally only partial. There is very little known about intergenerational and transgenerational effects, although studies on each stressor in isolation suggest that both positive and negative impacts may occur. Overall, the capacity for phenotypic plasticity in response to these two stressors is highly variable among species and extremely dependent on the specific context of the experiment, including the extent and timing of stressor exposure. This variability in the nature and extent of plasticity suggests that existing phenotypic plasticity is unlikely to adequately buffer fishes against the combined stressors of high temperature and hypoxia as our climate warms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it