Short-Term Chronic Toxicity of Copper to Hyalella azteca: Contrast in Terms of Equilibrating Diet, Diet Type, and Organic Matter Source
Bibliographic record
Abstract
The most up-to-date regulatory guidelines for establishing acute and chronic numeric limits for copper in freshwaters are based on a biotic ligand model for various species, but the model for Cu lacks data on dietary uptake. In addition, some common macroinvertebrate toxicity assay parameters are less representative of the ecosystem. We investigated the effects of diet and its type in the experimental setup and as an exposure pathway to an established amphipod (crustacean) Hyalella azteca (H. azteca) for Cu toxicity assays. We also investigated another overlooked aspect, the organic matter (OM) source. Our experiments compared the toxicity of pre-equilibrated and unequilibrated natural diets and a laboratory-favored diet in effluent and stormwater sources of organic matter adjusted to standard water characteristics. The experiments indicated a more toxic effect of the pre-equilibrated diet and natural dietary sources, and less toxic effects in the presence of effluent OM compared with stormwater OM, shifting LC50 or EC20 values by as much as 67% compared with the controls. The use of a pre-equilibrated natural diet in toxicity assays provides the advantage of producing toxicity data more representative of field conditions. Considering organic matter type, especially in dietary exposures, will better predict toxicity, accounting for copper complexation with OM from different sources and partitioning to the food supply. Adapting these ecologically relevant parameters in whole effluent toxicity testing or other assays will also provide safer regulatory oversite of discharges to surface waters.
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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 | 0.001 |
| 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".