Quantifying excess heavy metal concentrations in drainage basins using conservative mixing models
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Bibliographic record
Abstract
High concentrations of heavy metals and other pollutants in river sediments can have detrimental effects on the ecosystem and humans. The composition of river sediments throughout drainage basins therefore provides important information for environmental monitoring. An obvious first step for using river sediment compositions for monitoring is to quantify natural baseline concentrations. Once baselines have been quantified, is it straightforward to compare them to observations to identify excesses generated by, for example, anthropogenic inputs. In this study a new strategy for mapping element concentrations along rivers from discrete geochemical observations upstream is presented. We demonstrate our approach in a case study of the Clyde drainage basin in western Scotland, UK. First, continuous baselines are generated using simple forward models that conservatively mix source region concentrations along drainage networks. 1185 measurements of elemental concentrations from first-order streams are used to parameterise the source region. The calculated baselines are then compared to concentrations measured at 60 localities along the main channel of the Clyde river. For a range of major and trace elements (e.g., Mg, Sr, K, Mn), the downstream observations are in close agreement with baseline concentrations predicted by conservative mixing models. However, some heavy metal concentrations (Pb, Cu, Zn) tend to exceed predicted baseline concentrations. Therefore, the second part of our approach calculates element concentrations in source areas required to match the observed Pb, Cu and Zn concentrations measured along the river. An inverse approach is used to ‘unmix’ the observed concentrations utilising, again, a conservative mixing model. Model resolution is determined by the spatial distribution of the data. Resultant calculated natural baselines and heavy metal concentrations along the river can easily be compared to estimate excesses. We tentatively suggest that anthropogenic input to sediment composition along the river is equivalent to annual fluxes of 9.7, 1.5 and 5.7 t (106 g) of Pb, Cu and Zn, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it