Integrating Empirically Dissolved Organic Matter Quality for WHAM VI using the DOM Optical Properties: A Case Study of Cu–Al–DOM Interactions
Bibliographic record
Abstract
Metal speciation is important for understanding the toxicity of metals in aquatic systems, and can be predicted for mixtures of metals in presence of dissolved organic matter (DOM) with thermodynamic models such as WHAM VI. The influence of the DOM source (quality) has been demonstrated, but is presently neglected in predicting Cu activity (WHAM VI). Here we determined the effect of aluminum (Al) competition on copper (Cu) complexation for four different DOMs, from a high-colored DOM (more humic) to a low-colored DOM (less humic). In presence of Al, free Cu activities (defined as free ion activity) increased, consistent with competition between Cu and Al for the same binding sites on all DOM. The apparent competition decreased with increasing DOM color. Equilibrium modeling of Cu speciation with WHAM VI explained 49% of the variance in measured Cu activity. When modified to integrate DOM quality using a new empirical coefficient F related to DOM optical properties, Cu activities predicted from WHAM VI were significantly improved to about 80% of the observed variance explained. The effects of Al on Cu activity were well predicted by WHAM VI. Subsequently, we compared the relative effects of DOM concentration, DOM quality, and Al competition with other determinants of Cu activity represented in legislation and scientific literature (pH and hardness), and qualitatively ranked them by their influence on Cu activity for normal ranges encountered in fresh waters using WHAM VI. Our experimental results indicate that DOM quality is an important variable that should be included in predictive models of ion speciation (WHAM VI) and eco-toxicological models such as the biotic ligand model (BLM).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".