Validation of a chronic dietary cadmium bioaccumulation and toxicity model for <i>Hyalella azteca</i> exposed to field-contaminated periphyton and lake water
Bibliographic record
Abstract
A model previously developed in the laboratory to predict chronic bioaccumulation and toxicity of cadmium to Hyalella azteca from a diet of periphyton was validated by comparing predictions with measurements of Cd in two exposure scenarios: laboratory-cultured H. azteca exposed for 28 d to field-contaminated water and periphyton, and Cd measured in field-collected H. azteca. In both exposure scenarios, model predictions of bioaccumulation were shown to be robust; however, effects on Cd bioaccumulation from complexation with dissolved organic carbon (DOC) and inhibition of Cd bioaccumulation by Ca²⁺ must be incorporated into the model to permit its wider application. The model predicted that 80 to 84% of Cd in H. azteca came from periphyton when H. azteca were chronically exposed to dissolved Cd in lake water at 2.63 to 3.01 nmol/L and periphyton at 1,880 to 2,630 nmol/g ash-free dry mass. Dietary Cd contributed markedly to the model-predicted decrease in 28-d survival to 74% at environmental Cd concentrations in food and water. In reality, survival decreased to 10%. The lower than predicted survival likely was due to the higher nutritional quality of periphyton used to develop the model in the laboratory compared with the field-collected periphyton. Overall, this research demonstrated that Cd in a periphyton diet at environmental concentrations can contribute to chronic toxicity in H. azteca.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".