Incorporation of metal speciation data into QICAR models and application to data-poor technology-critical elements
Bibliographic record
Abstract
In a previous article, we developed quantitative ion character-activity relationships (QICARs) to relate the intrinsic properties of a metal to its acute toxicity towards freshwater aquatic organisms. These predictive tools were developed for a set of data-rich training elements and then applied to a representative selection of technology-critical elements (TCEs). The toxicity of the TCEs was reasonably well predicted, with most values located within the 95% prediction intervals. In this work, we have extended this approach to use the calculated metal speciation. Linear free energy relationships were used to estimate some of the needed thermodynamic constants. Using this information, we expressed the concentration resulting in a 50% effect level value as free metal activities and performed regression analyses. For the training metals, the determination coefficients slightly increased compared with those obtained using the total dissolved metal. As before, the log-transformed composite value of the covalent index (χm2r) was the best predictor of their acute toxicity towards algae and daphnids (χm = metal's electronegativity; r = ionic radius). However, for the TCEs, the regressions were much poorer, particularly when the predicted free metal ion concentrations were very low (e.g., < 10-18 M). We suggest that this result reflects the distinctive speciation of these metals, where (i) the free metal ion is present only at vanishingly low concentrations (the calculation of which is problematic) and (ii) in all but one case (Au(CN)2-), the metal's calculated speciation is dominated by neutral polyhydroxo species (e.g., Au(OH)30, Ge(OH)40…). In our view, this result does not undermine the use of QICARs. Rather, the use of QICARs revealed that free-ion activity could be inadequate for predicting the toxicity of the studied data-poor metals.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".