Modeling chronic dietary cadmium bioaccumulation and toxicity from periphyton to <i>Hyalella azteca</i>
Bibliographic record
Abstract
A chronic (28-d) Cd saturation bioaccumulation model was developed to quantify the Cd contribution from a natural periphyton diet to Cd in the freshwater amphipod Hyalella azteca. Bioaccumulation was then linked to chronic toxic effects. Juvenile H. azteca were exposed to treatments of Cd in water (3.13-100 nmol/L nominal) and food (389-26,300 nmol/g ash-free dry mass). Cadmium bioaccumulation, survival, and growth were recorded. Dietary Cd was estimated to contribute 21 to 31, 59 to 94, and 40 to 55% to bioaccumulated Cd in H. azteca exposed to treatments of Cd primarily in water, food, and food + water, respectively. Survival as a function of Cd lethal body concentration (679 nmol/g; 95% confidence limits, 617-747) was the most robust endpoint. Body concentration integrated all exposure routes. Based on the lethal body concentration, dietary Cd was predicted to contribute markedly (26-90%) to Cd in H. azteca. Cadmium concentration and food nutritional quality (biomass, chlorophyll a, total lipid, fatty acids, total protein) had no effect on H. azteca nutritional quality (total lipid, fatty acids, total protein) but did influence H. azteca dry weight. This research highlighted the importance of including a dietary component when modeling chronic effects of Cd and when refining endpoints for use in ecological risk assessment and water quality guidelines.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".