Data from: Exposure to warmer water and prenatal cortisol independently alter transcriptional plasticity during early development in Chinook salmon (<em>Oncorhynchus tshawytscha</em>)
Bibliographic record
Abstract
Freshwater ecosystems are changing rapidly from human induced rapid environmental change. Determining whether organisms have the adaptive capacity to respond to stressors and by which mechanisms remains a challenge. Within the Environmental-Match framework, we experimentally manipulated a maternal signal of environmental stress to see if it primed gene transcription plasticity to allow developing Chinook salmon (Oncorhynchus tshawytscha) to respond to water temperatures expected from climate modeling. Our OpenArray® chip approach across four functional groups (growth, metabolism, immune, stress) revealed that elevated temperatures induced a diversity of effects on gene transcriptional profiles at different early life stages for candidate genes at all levels of the hierarchical evaluation (complete gene sets, functional gene groups, individual gene transcriptional phenotypes). However, contrary to predictions, prenatal cortisol did not alter these transcriptional effects further and did not rescue temperature-driven increases in mortality. Results suggest that while exposure to signals of maternal stress have the capacity to induce complex life stage- and system-specific responses, they may not further mitigate existing responses induced by elevated water temperatures in Chinook salmon (possibly other species).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.021 |
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".