Development of Quantitative Ion Character<b>–</b>Activity Relationship Models to Address the Lack of Toxicological Data for Technology-Critical Elements
Bibliographic record
Abstract
Abstract Recent industrial developments have resulted in an increase in the use of so-called technology-critical elements (TCEs), for which the potential impacts on aquatic biota remain to be evaluated. In the present study, quantitative ion character–activity relationships (QICARs) have been developed to relate intrinsic metal properties to their toxicity toward freshwater aquatic organisms. In total, 23 metal properties were tested as predictors of acute median effect concentration (EC50) values for 12 data-rich metals, for algae, daphnids, and fish (with and without species distinction). Simple and multiple linear regressions were developed using the toxicological data expressed as a function of the total dissolved metal concentrations. The best regressions were then tested by comparing the predicted EC50 values for the TCEs (germanium, indium, gold, and rhenium) and platinum group elements (iridium, platinum, palladium, rhodium, and ruthenium) with the few measured values that are available. The 8 “best” QICAR models (adjusted r2 > 0.6) used the covalent index as the predictor. For a given metal ion, this composite parameter is a measure of the importance of covalent interactions relative to ionic interactions. Toxicity was reasonably well predicted for most of the TCEs, with values falling within the 95% prediction intervals for the regressions of the measured versus predicted EC50 values. Exceptions included Au(I) (all test organisms), Au(III) (algae and fish), Pt(II) (algae, daphnids), Ru(III) (daphnids), and Rh(III) (daphnids, fish). We conclude that QICARs show potential as a screening tool to review toxicity data and flag “outliers,” which might need further scrutiny, and as an interpolating or extrapolating tool to predict TCE toxicity. Environ Toxicol Chem 2021;40:1139–1148. © 2020 SETAC
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".