Effects of using synthetic sea salts when measuring and modeling copper toxicity in saltwater toxicity tests
Bibliographic record
Abstract
Synthetic sea salts are often used to adjust the salinity of effluent, ambient, and laboratory water samples to perform toxicity tests with marine and estuarine species. The U.S. Environmental Protection Agency (U.S. EPA) provides guidance on salinity adjustment in its saltwater test guidelines. The U.S. EPA suggests using commercial sea salt brands, such as Forty Fathoms (now named Crystal Sea Marinemix, Bioassay Grade), HW Marinemix, or equivalent salts to adjust sample salinity. Toxicity testing laboratories in Canada and the United States were surveyed to determine synthetic sea salt brand preference. The laboratories (n = 27) reported using four brands: Crystal Sea Marinemix (56%), HW Marinemix (22%), Instant Ocean (11%), and Tropic Marin (11%). Saline solutions (30 g/L) of seven synthetic sea salts were analyzed for dissolved copper and dissolved organic carbon (DOC) content. Brands included those listed above plus modified general-purpose salt (modified GP2), Kent Marine, and Red Sea Salt. The synthetic sea salts added from < 0.1 to 1.2 microg Cu/L to the solution. Solutions of Crystal Sea Marinemix had significantly elevated concentrations of DOC (range = 5.4-6.4 mg C/L, analysis of variance, Tukey, alpha = 0.05, p < 0.001) while other brands generally contained < 1.0 mg C/L. The elevated DOC in Crystal Sea Marinemix was expected to reduce copper toxicity. However, the measured dissolved copper effective concentration 50% (EC50) for Crystal Sea Marinemix was 9.7 microg Cu/L, similar to other tested sea salts. Analysis indicates that the organic matter in Crystal Sea Marinemix differs considerably from that of natural organic matter. On the basis of consistently adding little DOC and little dissolved copper, GP2 and Kent Marine are the best salts to use.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".