Metabolomics confirms that dissolved organic carbon mitigates copper toxicity
Bibliographic record
Abstract
Reductions in atmospheric emissions from the metal smelters in Sudbury, Canada, produced major improvements in acid and metal contamination of local lakes and indirectly increased dissolved organic carbon (DOC) concentrations. Metal toxicity, however, has remained a persistent problem for aquatic biota. Integrating high-throughput, nontargeted mass spectrometry metabolomics with conventional toxicological measures elucidated the mediating effects of dissolved organic matter (DOM) on the toxicity of Cu to Daphnia pulex-pulicaria, a hybrid isolated from these soft water lakes. Two generations of daphniids were exposed to Cu (0-20 μg/L) at increasing levels of natural DOM (0-4 mg DOC/L). Added DOM reduced Cu toxicity monotonically with median lethal concentration values increasing from 2.3 μg/L Cu without DOM to 22.7 μg/L Cu at 4 mg DOC/L. Reproductive output similarly benefited, increasing with DOM, yet falling with increases in Cu. Second generation reproduction was more impaired than the first generation. Dissolved organic matter had a greater influence than Cu on the metabolic status of the daphniids. Putative identification of metabolite peaks indicated that DOM elevation increased the metabolic energy status of the first generation animals, but this benefit was reduced in the second generation, although evidence of increased oxidative stress was detected. These results indicate that Sudbury's terrestrial ecosystems should be managed to increase aquatic DOM supply to enable daphniid colonists to both survive and foster stable populations.
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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".