Using gene expression to identify the most suitable environmental conditions for growth and metabolism of juvenile deepwater redfish (<i>Sebastes mentella</i>) in the Estuary and the Gulf of St. Lawrence
Bibliographic record
Abstract
Abstract Deepwater redfish Sebastes mentella will be among the most important resource-sustaining commercial bottom-fish fisheries in the years to come in the Estuary and Gulf of St. Lawrence (EGSL). In 2011, 2012, and 2013, three strong cohorts were recruited to the stock; their abundance in 2018 was 80 times higher than that of the 1993–2012 period. The main goal of this work was to deepen our knowledge of their growth regulation and metabolism in order to identify molecular indicators and determine how they are influenced by natural environmental conditions. Fish weight and water temperature explained 11% of the variation in relative mRNA levels of specific gene targets in liver and muscle among seven sites where deepwater redfish were captured in the EGSL. The relative expression of liver insulin-like growth factor-1 (igf-1) and white muscle A-chain lactate dehydrogenase (ldh-a) correlate positively with weight, whereas heavy chain muscle myosin (myo), heart citrate synthase (cs), and white muscle pyruvate kinase (pk) correlate negatively. The relative expression of heart cytochrome c oxidase subunit 1 (cox-1) and white muscle igf-1 receptor isoform a (igf-1ra) correlate negatively with temperature. Deepwater redfish from the estuary were smaller than those caught at other sites. Since the growth potential of deepwater redfish was strongly correlated with temperature (being enhanced by higher temperatures), this study suggests an ecological advantage for this species in a climate-warming context.
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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) | 0.000 | 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".