Physiological effects of five different marine natural organic matters (NOMs) and three different metals (Cu, Pb, Zn) on early life stages of the blue mussel (<i>Mytilus galloprovincialis</i>)
Bibliographic record
Abstract
Metals are present in aquatic environments as a result of natural and anthropogenic inputs, and may induce toxicity to organisms. One of the main factors that influence this toxicity in fresh water is natural organic matter (NOM) but all NOMs are not the same in this regard. In sea water, possible protection by marine NOMs is not well understood. Thus, our study isolated marine NOMs by solid-phase extraction from five different sites and characterized them by excitation-emission fluorescence analysis—one inshore (terrigenous origin), two offshore (autochthonous origin), and two intermediate in composition (indicative of a mixed origin). The physiological effects of these five NOMS alone (at 8 mg/L), of three metals alone (copper, lead and zinc at 6 µg Cu/L, 20 µg Pb/L, and 25 µg Zn/L respectively), and of each metal in combination with each NOM, were evaluated in 48-h exposures of mussel larvae. Endpoints were whole body Ca2++Mg2+-ATPase activity, carbonic anhydrase activity and lipid peroxidation. By themselves, NOMs increased lipid peroxidation, Ca2++Mg2+-ATPase, and/or carbonic anhydrase activities (significant in seven of 15 NOM-endpoint combinations), whereas metals by themselves did not affect the first two endpoints, but Cu and Pb increased carbonic anhydrase activities. In combination, the effects of NOMs predominated, with the metal exerting no additional effect in 33 out of 45 combinations. While NOM effects varied amongst different isolates, there was no clear pattern with respect to optical or chemical properties. When NOMs were treated as a single source by data averaging, NOM had no effect on Ca2++Mg2+-ATPase activity but markedly stimulated carbonic anhydrase activity and lipid peroxidation, and there were no additional effects of any metal. Our results indicate that marine NOMs may have direct effects on this model marine organism, as well as protective effects against metal toxicity, and the quality of marine NOMs may be an important factor in these actions.
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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".