Molecular indicators of warming and other climate stressors in larval Pacific cod
Bibliographic record
Abstract
Recent marine heatwaves in the Gulf of Alaska negatively impacted Pacific cod ( Gadus macrocephalus) through a series of failed year classes and poor recruitment to the fishery. Experimental work by Slesinger et al. (2024) corroborated the hypothesis that warming directly impacts recruitment by increasing larval mortality rates. In this companion study, we applied transcriptomics with larvae from Slesinger et al. (2024) to better understand how warming affected their physiology and identify potential mechanisms contributing to mortality. RNASeq data reveal that warm-exposed larvae have unique gene expression profiles that may reflect high levels of inflammation, lipid dysregulation or depletion, and altered development of visual systems and neurological pathways. Warming may therefore cause a metabolic mismatch whereby energy-demanding activities (development, inflammation, growth) exceed energy production capacity despite access to prey. We also report the less pronounced transcriptional differences in larvae exposed to cold, acidification, and a combination of stressors reflecting future climate scenarios. This information will guide future genetic and experimental work that will ultimately inform recruitment forecasts in years with conditions similar to those tested here.
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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".