Particulate organic carbon and nitrogen export from major Arctic rivers
Bibliographic record
Abstract
Abstract Northern rivers connect a land area of approximately 20.5 million km2 to the Arctic Ocean and surrounding seas. These rivers account for ~10% of global river discharge and transport massive quantities of dissolved and particulate materials that reflect watershed sources and impact biogeochemical cycling in the ocean. In this paper, multiyear data sets from a coordinated sampling program are used to characterize particulate organic carbon (POC) and particulate nitrogen (PN) export from the six largest rivers within the pan‐Arctic watershed (Yenisey, Lena, Ob', Mackenzie, Yukon, Kolyma). Together, these rivers export an average of 3055 × 109 g of POC and 368 × 109 g of PN each year. Scaled up to the pan‐Arctic watershed as a whole, fluvial export estimates increase to 5767 × 109 g and 695 × 109 g of POC and PN per year, respectively. POC export is substantially lower than dissolved organic carbon export by these rivers, whereas PN export is roughly equal to dissolved nitrogen export. Seasonal patterns in concentrations and source/composition indicators (C:N, δ13C, Δ14C, δ15N) are broadly similar among rivers, but distinct regional differences are also evident. For example, average radiocarbon ages of POC range from ~2000 (Ob') to ~5500 (Mackenzie) years before present. Rapid changes within the Arctic system as a consequence of global warming make it challenging to establish a contemporary baseline of fluvial export, but the results presented in this paper capture variability and quantify average conditions for nearly a decade at the beginning of the 21st century.
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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".