Influence of the bordering shelves on nutrient distribution in the Arctic halocline inferred from water column nitrate isotopes
Bibliographic record
Abstract
Abstract The East Siberian Sea and contiguous western Arctic Ocean basin are characterized by a subsurface nutrient maximum in the halocline, generally attributed to both Pacific inflow and intensive remineralization in shelf bottom waters that are advected into the central basin. We report nitrogen and oxygen isotopic measurement of nitrate from the East Siberian Sea and western Eurasian Basin, in order to gain insight into how nitrate is processed by the microbial community and redistributed in the Arctic Ocean. A large decoupling between nitrate δ15N and δ18O is reported, increasing and decreasing upward from the Atlantic temperature maximum layer toward the surface, respectively. A correlation between water and nitrate δ18O indicates that most of the nitrate (> 60%) at the halocline has been regenerated within the Arctic Ocean. The increase in nitrate δ15N correlates with the fixed N deficit, indicating a causal link between the loss of fixed N and the 15N enrichment. This suggests that a significant share of benthic denitrification is driven by nitrate supplied by remineralization and partial nitrification, allowing residual 15N‐enriched ammonium to diffuse out of the sediments. By increasing nutrient concentrations and fixed N deficit in shelf bottom waters, this imprint is attenuated offshore following advection into the halocline by nitrate regeneration and mixing. Estimation of the sedimentary isotope effect related to benthic denitrification yields values in the range of 2.4–3.8‰, with its magnitude driven by both the degree of coupling between remineralization and nitrification, and fixed N concentrations in shelf bottom waters.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".