In the Search of Sublethal Biomarkers: Metabolomic and Lipidomic Profiling of Arctic Copepods Facing Ocean Acidification and Pyrene Pollution
Bibliographic record
Abstract
Historically relatively sheltered from human disturbance, Arctic regions now face increasing threats from expanding maritime routes and industrial activities. These pressures add to existing environmental stressors, raising concerns about the conservation of fragile polar ecosystems, which are particularly vulnerable to cumulative impacts such as ocean acidification (OA) and widespread pollution. Combined stressors may cause unpredictable non-additive effects on marine ectotherms. Furthermore, by using traditional approaches limited to whole-organism levels, such as metabolic rates and mortality, we have overlooked sublethal, early-signals of physiological failure occurring at the cellular level. Therefore, we aimed at exploring the molecular responses of a key Arctic zooplankton species to different environmentally-realistic doses of a toxic polycyclic aromatic hydrocarbon under an OA scenario. For this purpose, the metabolome and lipidome of field-collected copepodid IV specimens of the copepod Calanus glacialis were profiled after two and ten days of exposure within an orthogonal design of four pyrene concentrations (0, 10, 100 and 200 nM) and two levels of OA (ambient and low pH). Multivariate analyses evidence an interaction between OA and pyrene contamination over time, albeit pyrene is a major driver. OA seemingly shifts the metabolomic dose response to pyrene compared to ambient pH. Further integrative analyses will help to decipher the functional consequences of such molecular alterations, to identify early-signals of physiological failure. Implementing multilayer knowledge from such multifactorial experimental approaches will be critical in improving our ability to predict the fate of Arctic species in future, more disturbed polar oceans.
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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 | 0.001 | 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".