An aquatic toxicological evaluation of sulfate: The case for considering hardness as a modifying factor in setting water quality guidelines
Bibliographic record
Abstract
Elevated concentrations of sulfate occur commonly in anthropogenically impacted and natural waters. However, water quality guidelines (WQG) have not been developed in many jurisdictions, and chronic toxicity data are scarce for this anion. A variety of test organisms, including species of invertebrate, fish, algae, moss, and an amphibian, were tested for chronic toxicity to develop a robust dataset that could be used to develop WQGs. As an example of how these data might be used to establish guidelines, calculations were performed using two standard procedures: a species sensitivity distribution (SSD) approach, following methods employed in developing Canadian WQGs, and a safety factor approach, according to procedures typically used in the development of provincial WQGs in British Columbia. The interaction of sulfate toxicity and water hardness was evaluated and incorporated into the calculations, resulting in separate values for soft (10-40 mg/L), moderately hard (80-100 mg/L) and hard water (160-250 mg/L). The resulting values were 129, 644, and 725 mg/L sulfate, respectively, following the SSD approach, and 75, 625, and 675 mg/L sulfate, following the safety factor approach.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".