Predicting Water Quality Criteria for Protecting Aquatic Life from Physicochemical Properties of Metals or Metalloids
Bibliographic record
Abstract
Metals are widely distributed pollutants in water and can have detrimental effects on some aquatic life and humans. Over the past few decades, the United States Environmental Protection Agency (U.S. EPA) has published a series of criteria guidelines, which contain specific criteria maximum concentrations (CMCs) for 10 metals. However, CMCs for other metals are still lacking because of financial, practical, or ethical restrictions on toxicity testing. Herein, a quantitative structure activity relationship (QSAR) method was used to develop a set of predictive relationships, based on physical and chemical characteristics of metals, and predict acute toxicities of each species for five phyla and eight families of organisms for 25 metals or metalloids. In addition, species sensitivity distributions (SSDs) were developed as independent methods for determining predictive CMCs. The quantitative ion character-activity relationships (QICAR) analysis showed that the softness index (σp), maximum complex stability constants (log -β(n)), electrochemical potential (ΔE(0)), and covalent index (X(m)(2)r) were the minimum set of structure parameters required to predict toxicity of metals to eight families of representative organisms. Predicted CMCs for 10 metals are in reasonable agreement with those recommended previously by U.S. EPA within a difference of 1.5 orders of magnitude. CMCs were significantly related to σp (r(2) = 0.76, P = 7.02 × 10(-9)) and log -β(n) (r(2) = 0.73, P = 3.88 × 10(-8)). The novel QICAR-SSD model reported here is a rapid, cost-effective, and reasonably accurate method, which can provide a beneficial supplement to existing methodologies for developing preliminarily screen level toxicities or criteria for metals, for which little or no relevant information on the toxicity to particular classes of aquatic organisms exists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".