Changes in the Composition of Nitrogen Yields in Large Arctic Rivers Linked to Temperature and Precipitation
Bibliographic record
Abstract
Abstract Global trends in river nitrogen yields reflect human distortion of the global nitrogen cycle. Climate change and increasing agricultural intensity are projected to enhance river nitrogen yields in temperate watersheds and impair downstream water quality. However, little is known about the environmental drivers of nitrogen yields in major Arctic rivers, which have experienced rapid climatic changes and are important conduits of nutrients and organic matter to the Arctic Ocean. Here we analyze trends in nitrogen yields in the six largest Arctic rivers between 2003 and 2023 and develop generalized additive models to elucidate the watershed characteristics and climatic processes associated with observed spatial and interannual variability. We found significant increases in dissolved organic nitrogen yield and/or declines in dissolved inorganic nitrogen yield in four of the six rivers. While temperature and precipitation, via their relationships to discharge, enhance dissolved nitrogen yields, we attribute the diverging trends to the responses of inorganic and organic nitrogen to temperature via effects on permafrost free extent. Spatially, we attribute differences in nitrogen yields across watersheds to differences in land cover and temperature. Shifts in the amount and composition of river nitrogen yields will impact the balance between primary productivity and heterotrophy in nitrogen limited coastal Arctic Ocean ecosystems. Results from this work highlight the importance of climate‐driven changes in temperature and precipitation on river nitrogen yields in large Arctic rivers and motivate further investigation into how permafrost loss and hydrological shifts interact to drive water quality and biogeochemical cycling in the region.
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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) | 0.000 | 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".