Heterogeneity in physicochemical properties explains differences in silver toxicity amelioration by natural organic matter to <i>Daphnia magna</i>
Bibliographic record
Abstract
Recently collected data have shown that natural organic matter (NOM) source is an important parameter influencing the toxicity of silver to the freshwater crustacean Daphnia magna. The present study attempted to correlate the physicochemical properties of 11 naturally isolated and commercially available NOM sources with their ameliorative effects. The protection offered by these samples was standardized to the protective effect of Aldrich humic acid using geochemical modeling approaches that accounted for associated changes in water chemistry and, consequently, silver speciation. The protective ability of NOM was not correlated with reactive sulfide or nitrogen content, which are considered to be strong silver-binding ligands. Color (specific absorbance coefficient) was positively correlated with protection but narrowly eluded statistical significance. The peak wavelength of emission fluorescence following excitation at 370 nm and the fluorescence index values of NOM samples were significantly correlated with protective effects, suggesting that aromatic carbon content may govern the ameliorative actions of NOM. Simple optical properties may therefore act as a suitable indicator for the ability of a given NOM to protect against waterborne silver toxicity to D. magna as long as changes in water chemistry and, thus, silver speciation, are considered.
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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) | 0.000 | 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".