Nitrogen dynamics in low-arctic streams are linked to terrestrial vegetation cover
Bibliographic record
Abstract
The Arctic is experiencing rapid climatic changes, leading to landscape shifts, including increased vegetation cover (greening) and altered nutrient dynamics. This study examines the relationship between catchment vegetation cover, measured by the normalized difference vegetation index (NDVI), and dissolved inorganic nitrogen and dissolved organic nitrogen concentrations in streams in Kobbefjord region in low-Arctic West Greenland. Water samples from streams were collected weekly across three catchments with varying vegetation cover during summer 2023. Our results showed a significant negative relationship between catchment NDVI and stream nitrate (NO3 −) concentrations. The results support what has previously been found in high-Arctic streams in Northeast Greenland and thus expand the generality of this pattern in a broader Arctic context. The results suggest that greening in low-Arctic areas could reduce the annual N export from terrestrial to aquatic systems. However, the variation in export during the summer season is driven by discharge. Thus, the future projected greening and increased summer precipitation will most likely alter nutrient availability and primary production in the coastal ecosystems of the Arctic Ocean. Such changes may have significant ecological consequences not only for the coastal ecosystems but also for the Arctic communities that rely on these ecosystems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".