Contrasting copper concentrations and isotopic compositions in two Great Lakes watersheds
Bibliographic record
Abstract
Copper (Cu) stable isotopes can elucidate the biogeochemical controls and sources governing Cu dynamics in aquatic environments, but their application in larger rivers and catchments remains comparatively scarce. Here, we use major and trace element hydrogeochemical data, Cu isotope analyses, and mixing modeling, to assess Cu loads and sources in two major river systems in Ontario, Canada. In both the Spanish River and Trent River catchments, aqueous hydrochemical compositions appeared reasonably consistent, but Cu concentrations were more variable spatially. In the Spanish River, waters near (historic) industrial mining activities displayed positive Cu isotope compositions (δ65CuSRM-976 between +0.75 ‰ and +1.01 ‰), but these signatures were gradually attenuated downstream by mixing with natural background waters (δ65Cu −0.65 ‰ to −0.16 ‰). In contrast, Trent River waters exhibited more irregular in-stream Cu isotope patterns (δ65Cu from −0.75 ‰ to +0.21 ‰), beyond the variability in Cu isotope signatures observed for adjacent agricultural soils (δ65Cu between −0.26 ‰ and +0.30 ‰) and lacking spatial correlation, reflective of the more diffuse sourcing and entwined endmember contributions to Cu loads in this catchment. This work shows that metal stable isotopes may improve our understanding of the sources and baseline dynamics of metals, even in large river systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".