Development of an Acute Biotic Ligand Model for Ni Toxicity to Daphnia pulex in Soft Water: Effects of Ca, Mg, Na, K, Cl, pH and Dissolved Organic Matter
Bibliographic record
Abstract
In this study the influence of several water chemistry parameters on the toxicity of Ni to Daphnia pulex in soft water were tested. A reconstituted soft water (pH 7.8, hardness 31.5 mg/L CaCO3) was used as the basis for culture and testing. Daphnia pulex was chosen as a typical cladoceran, one which can be acclimated to very soft water. An understanding of the influence of water chemistry on Ni toxicity in soft water is relevant because metals have higher bioavailability in soft water. The 48h EC50 in the reconstituted soft water (RSW) was 974 μg/L (16 μM) dissolved Ni (95%CI 830- 1081 μg/L). The following factors were examined for their potential for modifying Ni toxicity: Ca, Mg, Na, K, Cl, pH (3 different approaches used) and natural organic matter (NOM, 2 sources tested). Both Ca and Mg protected against Ni toxicity and the relative effect was greater for Ca. Varying the concentrations of Na, Cl or K did not alter the toxicity of Ni. Tests at different pH showed that as pH increased, Ni toxicity decreased. When the test solution pH was adjusted with the organic buffer 3-morpholinepropanesulfonic acid, there was a clear correlation between increasing pH and increasing EC50. The pH tests using bicarbonate to adjust pH did not show this relationship as clearly. Both types of NOM showed a protective effect on Ni toxicity with Nordic Reservoir NOM having a 4 fold greater effect than that of Suwannee River NOM. This research illustrated that the effect of alterations in water chemistry were generally as predicted within the context of the biotic ligand model (BLM) approach. The data provide the information required to develop a BLM for the acute effects of Ni in soft water.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".