Lifelong Exposure to PCBs in the Remote Norwegian Arctic Disrupts the Plasma Stress Metabolome in Arctic Charr
Bibliographic record
Abstract
Lake Ellasjøen on the remote Norwegian island of Bjørnøya is populated by Arctic charr (Salvelinus alpinus) having 20-fold higher body burdens of polychlorinated biphenyls (PCB) compared to charr from the neighboring Lake Laksvatn. This provides a natural setting to test the hypothesis that lifelong exposure to PCBs compromises the energy metabolism in this northernmost living salmonid. To test this, blood was sampled from charr from both lakes immediately after capture and following a 1 h handling and confinement stressor to assess possible differences in their energy metabolism and energy substrate mobilization, respectively. The plasma metabolome of charr was assessed by metabolite detection/separation with LC-MS. Plasma metabolite profiles revealed differences in key pathways involved in amino acid metabolism between charr from each lake, underscoring an impact of PCBs on energy metabolism in Arctic charr residing in Lake Ellasjøen. Subjecting charr from either lake to an acute stressor altered the plasma metabolite profiles and revealed distinct stress metabolome in Lake Ellasjøen charr, suggesting a reduced metabolic capacity. Taken together, lifelong exposure to PCBs in Ellasjøen charr disrupts the plasma metabolome, and may impair the adaptive metabolic response to stressors, leading to a reduced fitness.
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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".