Effect of chemical pollution representative of an industrial port on the embryonic success of the blue mussel, <i>Mytilus edulis</i> : a metabolomic approach
Bibliographic record
Abstract
With the growing demand for international maritime trade, there is an urgent need to improve our knowledge of the impact of shipping on marine organisms to develop effective mitigation strategies. Bivalves are important organisms in intertidal ecosystems where maritime harbors are situated. In this work, we evaluated the response of mussel larvae exposed to chemical contaminants associated with maritime traffic. We focused on embryogenesis because the initial ontogenic stages are particularly susceptible to stressful events that are crucial for maintaining community structure. Thus, embryos of the blue mussel, Mytilus edulis Linnaeus, 1758, from two spawning events following natural gonad maturation, were exposed to a cocktail of contaminants (copper, mercury, lead, and hydrocarbons) representative of a port environment until the end of embryogenesis. Our results show a different metabolomic response of D-larvae to contamination depending on the embryogenic success. Contaminated larvae had greater energy requirements and appeared to successfully employ a variety of mechanisms involving antioxidant biosynthesis and energy metabolism restructuring to mitigate toxic effects on cells and developing tissues. This work demonstrates the vulnerability of mussel larvae to an environment with high maritime traffic, even though exposure to contaminants did not ultimately affect the success of embryogenesis.
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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".