The effect of calcium on acute sodium chloride toxicity in <i>Daphnia</i> species
Bibliographic record
Abstract
Chloride concentrations in freshwater are rising, with toxic effects on aquatic life. In temperate regions with cold winters, road salt used for deicing paved surfaces is a primary cause. There is evidence that water hardness can modify salt toxicity, but data are insufficient to inform policy. Because calcium is a primary ion influencing water hardness and there is widespread calcium decline in lakes, we examined the effects of varying calcium concentrations on acute salt toxicity in three Daphnia species to gain a greater understanding of the water hardness-salt toxicity relationship. We conducted 48-hr acute sodium chloride (NaCl) toxicity tests, using chloride concentrations as our metric, on neonates less than 24 hrs old in six calcium treatments: 1.5 to 128 mg/L (hardness ∼7 to 323 mgCaCO3/L). We determined the effective concentration of chloride that was lethal to 10%, 25%, and 50% of the sample populations from each iso-female line in each calcium treatment. Acute NaCl toxicity decreased as calcium concentrations increased. The relationship between NaCl toxicity and calcium concentration differed among Daphnia, such that Daphnia catawba and Daphnia pulex were more sensitive to NaCl in lower calcium treatments and less sensitive in higher calcium treatments compared to Daphnia pulicaria. Our results provide evidence that water quality guidelines are not protective enough for aquatic life in very soft water (≤3 mg Ca2+/L, 11.3 mg CaCO3/L) because most ECxx values we found for Daphnia were significantly lower than Canada's national guidelines for short-term chloride exposure. There are already many lakes with calcium concentrations below 3 mg/L, and global widespread calcium decline may put more aquatic ecosystems at risk of experiencing NaCl toxicity.
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 | 0.001 |
| 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".