The Processing and Impact of Dissolved Riverine Nitrogen in the Arctic Ocean
Bibliographic record
Abstract
Although the Arctic Ocean is the most riverine-influenced of all of the world’s oceans, the importance of terrigenous nutrients in this environment is poorly understood. This study couples estimates of circumpolar riverine nutrient fluxes from the PARTNERS (Pan-Arctic River Transport of Nutrients, Organic Matter, and Suspended Sediments) Project with a regionally configured version of the MIT general circulation model to develop estimates of the distribution and availability of dissolved riverine N in the Arctic Ocean, assess its importance for primary production, and compare these estimates to potential bacterial production fueled by riverine C. Because riverine dissolved organic nitrogen is remineralized slowly, riverine N is available for uptake well into the open ocean. Despite this, we estimate that even when recycling is considered, riverine N may support 0.5–1.5 Tmol C year −1 of primary production, a small proportion of total Arctic Ocean photosynthesis. Rapid uptake of dissolved inorganic nitrogen coupled with relatively high rates of dissolved organic nitrogen regeneration in N-limited nearshore regions, however, leads to potential localized rates of riverine-supported photosynthesis that represent a substantial proportion of nearshore production.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".