ASSESSMENT OF THE EFFECT OF WATER QUALITY ON COPPER TOXICITY IN<i>HYALELLA AZTECA</i>
Bibliographic record
Abstract
The objective of this study was to test the hypothesis that when standard artificial media 5-salt culture water (SAM-5S) is used to test sediment toxicity of much lower ionic-strength aquatic ecosystems, the resulting toxicity estimates are lower than if the tests had been conducted in water of comparable ionic strength. Results showed that this concern was unfounded for testing of copper toxicity to Hyalella azteca (H. azteca) in Ottawa River water. Sediment testing is often conducted using a standard water that is prepared in the laboratory. However, this water may have an ionic strength that is different than local water bodies. It follows that laboratory results using the standard water may be unrepresentative. A study was undertaken to assess the copper tolerance of 2 strains of H. azteca in SAM-5S, diluted SAM-5S (similar in electrical conductivity to Ottawa River water), and Ottawa River water. Acute (96 h) copper toxicity tests were conducted with 9–16 day-old H. azteca. For a given water type, the 2 strains of H. azteca yielded comparable responses to copper. The highest copper tolerance was found in Ottawa River water (closely followed by SAM-5S), whereas the lowest copper tolerance was found in diluted SAM-5S. Our results suggest that sediment toxicity is not lowered by the higher ionic strength of SAM-5S and that sediment toxicity tests of Ottawa River sediments, conducted with SAM-5S, can be used to estimate the in situ toxicity of the sediments.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".