Distribution of rare earth elements and their signatures from the Mackenzie River delta to the abyssal Arctic Ocean
Bibliographic record
Abstract
The Mackenzie River is North America's largest contributor of freshwater and sediment to the Arctic Ocean. Here, we evaluate the potential of rare earth elements (REE) as tracers of its sediment sources and fate, from the river mouth to the deep Arctic Ocean. We collected sediment cores from 21 sites, from the delta to the marine shelves, slopes and basins and measured the spatial and down-core distribution of total, leached and residual REE concentrations. Our results show that the proportion of leached REE is highest in the delta. This proportion decreases with distance from the river, suggesting mixing with other sediment sources, REE loss to the residual phase, or REE scavenging via adsorption and complexation in coastal waters. Normalized REE concentrations plotted against their atomic number provide regional signatures. The leached REE signatures indicate medium REE enrichment in the Mackenzie Delta, an enrichment that diminishes with distance from the delta. We then used a similarity index (SI) to investigate the divergence amongst REE signatures, with riverine and deep marine basin values as endmembers for the calculation. Our results highlight the influence of the Mackenzie Region sediments on the Beaufort Sea margin. Overall, our findings demonstrate that REE are relevant tracers for identifying sediment sources and that tracking REE distribution from the delta to the deep Arctic Ocean offers additional insights into sediment transport mechanisms. • 226 samples across 21 sites were analyzed for rare earth elements concentrations. • Highest leached proportion in Mackenzie River sediments. • The Mackenzie River signatures present a medium-REE enrichment. • The tracer shows a decreased influence of Mackenzie sediments with distance. • Positive Ce anomaly in the deep Amerasian Basin consistent with previous reports.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".