Geochemical Equilibrium Modelling of the Aqueous Speciation of Select Trace Elements in the Great Lakes
Bibliographic record
Abstract
The behaviour and fate of trace elements in surface waters are greatly affected by their chemical form in solution, but the aqueous speciation of dissolved trace elements in the North American Great Lakes has received relatively little attention. Here, we present results from geochemical equilibrium modelling with 2021 surface water quality data to examine the spatiotemporal dynamics of trace element speciation in the Great Lakes. The relative abundance of aqueous trace element species appeared consistent with variability in solution chemistry and followed basin-wide trends in pH, alkalinity, salinity, and nutrient levels. The speciation of alkali metals was dominated by free monovalent cations, and that of oxyanion-forming elements by oxoacids, whereas significant fractions (>1%) of other aqueous complexes were also evident for rare earth elements (e.g., Ce and Gd as carbonates), alkaline earth metals (e.g., Sr as sulfates), or transition metals (e.g., Zn as phosphates). Spatially, differences in the relative abundance of aqueous trace element species were <2 orders of magnitude, with the highest variation (~50-fold) occurring for select chloride-complexes, resulting from upstream-to-downstream salinity increases in the basin. Finally, simulations of various future water quality scenarios (e.g., decreasing P levels, increasing temperature and salinity) suggest that the speciation of most trace elements is robust temporally as well. This study demonstrates how considering aqueous speciation may help improve the understanding of trace element dynamics and support water quality management in the Great Lakes.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".