Heterogeneity of natural organic matter amelioration of silver toxicity to <i>Daphnia magna</i>: Effect of source and equilibration time
Bibliographic record
Abstract
Despite the heterogeneity of natural organic matter (NOM) in the aquatic environment, current models that predict metal toxicity to aquatic biota treat these important metal-complexing agents in a homogeneous manner. In this investigation, the ability of 11 commercial and naturally isolated NOM samples to ameliorate silver toxicity to the freshwater crustacean Daphnia magna was examined. The commercially available Aldrich humic acid (AHA) increased the 48-h median lethal concentration for daphnid neonates from nominally NOM-free levels of 0.29 to 3.80 microg/L (at 6.9 mg C/L) in a concentration-dependent manner. Three of the tested samples exhibited similar protective effects, but the additional seven NOM samples displayed significantly stronger ameliorative actions. In fact, four samples of both commercial and naturally isolated origin demonstrated greater than fourfold increases in protection compared to that of AHA. Additional investigations showed that increased silver-AHA equilibration time resulted in decreased toxicity. Increased equilibration time also decreased whole-body silver accumulation at NOM levels less than 1 mg C/L. The present results suggest that heterogeneity of NOM and silver-NOM equilibration time will have to be accounted for in future models of silver toxicity to D. magna and that laboratory toxicity testing using NOM and metals should account for the effects of metal-NOM equilibration time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".