NMR-Based Metabolomics of <i>Daphnia Magna</i>: Insights into Aquatic Ecosystem Health
Bibliographic record
Abstract
Metabolomics has gained traction as an essential tool to understand the responses of organisms to environmental stressors. Because of its importance as an aquatic ecosystem member and its ubiquity in ecotoxicology studies, the crustacean zooplankter Daphnia magna has been the focus of many metabolomic studies. An overview of the metabolomics research conducted using D. magna with NMR is provided in this article. Most of the studies are premised on providing a biochemical context to the D. magna responses to anthropogenic pollutants, including metals and a range of commonly used household and industrial compounds. However, beyond pollutant stress, there is also research on other environmental stressors, such as poor diet quality, salinity, and bacterial infection. In addition, in vivo analyses of D. magna using NMR provide a new avenue for research, where metabolites can be monitored in real time. Changes in the metabolome are seen at levels well below mortality thresholds and show that metabolomics is a more sensitive gauge of stress than the apical endpoint tests currently used in aquatic toxicology.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".