Dissolved Major and Trace Elements in the Largest Eurasian Arctic Rivers: Ob, Yenisey, Lena, and Kolyma
Bibliographic record
Abstract
In contrast to fairly good knowledge of dissolved carbon and major elements in great Arctic rivers, seasonally resolved concentrations of many trace elements remain poorly characterized, hindering assessment of the current status and possible future changes in the hydrochemistry of the Eurasian Arctic. To fill this gap, here we present results for a broad suite of trace elements in the largest rivers of the Russian Arctic (Ob, Yenisey, Lena, and Kolyma). For context, we also present results for major elements that are more routinely measured in these rivers. Water samples for this study were collected during an international campaign called PARTNERS from 2004 through 2006. A comparison of element concentrations obtained for Arctic rivers in this study with average concentrations in the world’s rivers shows that most elements in the Arctic rivers are similar to or significantly lower than the world average. The mineral content of the three greatest rivers (Ob, Yenisey, and Lena) varies within a narrow range (from 107 mg/L for Yenisey to 123 mg/L for Ob). The Kolyma’s mineral content is significantly lower (52.4 mg/L). Fluxes of all major and trace elements were calculated using average concentrations and average water discharge for the 2004–2006 period. Based on these flux estimates, specific export (i.e., t/km2/y) for most of the elements was greatest for the Lena, followed by the Yenisey, Ob, and Kolyma in decreasing order. Element pairwise correlation analysis identified several distinct groups of elements depending on their sources and relative mobility in the river water. There was a negative correlation between Fe and DOC concentration in the Ob River, which could be linked to different sources of these components in this river. The annual yields of major and trace elements calculated for each river were generally consistent with values assessed for other mid-size and small rivers of the Eurasian subarctic.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".