An effects addition model based on bioaccumulation of metals from exposure to mixtures of metals can predict chronic mortality in the aquatic invertebrate<i>hyalella azteca</i>
Bibliographic record
Abstract
Chronic toxicity tests of mixtures of 9 metals and 1 metalloid (As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Tl, and Zn) at equitoxic concentrations over an increasing concentration range were conducted with the epibenthic, freshwater amphipod Hyalella azteca. The authors conducted 28-d, water-only tests. The bioaccumulation trends changed for 8 of the elements in exposures to mixtures of the metals compared with individual metal exposures. The bioaccumulation of Co and Tl were affected the most. These changes may be due to interactions between all the metals as well as interactions with waterborne ligands. A metal effects addition model (MEAM) is proposed as a more accurate method to assess the impact of mixtures of metals and to predict chronic mortality. The MEAM uses background-corrected body concentration to predict toxicity. This is important because the chemical characteristics of different waters can greatly alter the bioavailability and bioaccumulation of metals, and interactions among metals for binding at the site of action within the organism can affect body concentration. The MEAM accurately predicted toxicity in exposures to mixtures of metals, and predicted results were within a factor of 1.1 of the observed data, using 24-h depurated body concentrations. The traditional concentration addition model overestimated toxicity by a factor of 2.7.
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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 | 0.001 |
| 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 | 0.001 |
| 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".