Drivers of rare earth elements (REEs) and radionuclides in changing subarctic (Nunavik, Canada) surface waters near a mining project
Bibliographic record
Abstract
The emergence of mining projects for rare earth elements (REEs) in response to rising global demand and geopolitical factors introduces environmental concerns, such as the suspected release of anthropogenic REEs to aquatic systems and the coexistence of radionuclides (U, Th). Northern regions confront heightened challenges from limited research and accelerated climate change. Drivers of REEs in surface waters (including George and Koroc rivers, their tributaries, and thermokarst lakes) were studied (2017–2023) in subarctic Canada within a climate transition zone, near a prospective REE mine. Dissolved REEs (<0.45 μm) correlated positively with Al, Fe, Th, U, Cl– and DOC. A novel relationship with water temperature demonstrated an approximate 10-fold decrease in REE concentrations over the environmental gradient (2–20 ℃), suggesting complex implications for REE speciation under climate pressures. Optical analyses further predicted REEs were mobilized by humic-rich, terrestrial DOC, with correlations presenting a possible co-transport with Al, Fe and Th. Relationships for redox-sensitive Ce anomalies (Ce/Ce* = 0.18–1.2) with multi-valent trace metals (Al, Fe, Ti) and DOC were suggestive of a preferential adsorption of Ce by inorganic colloids in low-DOC systems. Findings emphasized the potential for changes in REE geochemistry with ongoing northern surface warming and vegetation shifts. Rare earth elements (REEs) and co-occurring radionuclides present environmental risks to aquatic systems and were investigated in subarctic surface waters (rivers, lakes) before mining exploitation. REEs and Ce fractionation were driven by Al, Fe, Ti, U, Th and DOC, suggesting a redox control and potential co-transport. Novel correlations with water temperature and mobilization by terrestrial colloids highlighted changes to REE geochemistry and consequentially, bioavailability, with continued climate warming.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".