A Numerical Model Analysis of the Mean and Seasonal Nitrogen Budget on the Northeast U.S. Shelf
Bibliographic record
Abstract
Abstract The supply of nitrogen is a primary limiting factor for the productivity of the Northeast United States (NEUS) continental shelf. In this study, a 12‐year (1996–2007) retrospective physical‐biogeochemical simulation over the Northwest Atlantic was used to analyze the mean and seasonal NEUS shelf nitrogen budget, including the connections between shelf subregions: the Gulf of Maine/Georges Bank (GoM/GB), and the Mid‐Atlantic Bight (MAB). The model captures the primary mean and seasonal patterns of shelf circulation, nitrate, and plankton dynamics. Results confirm aspects of previous nitrogen budget analyses, including the dominance of offshore nitrogen influxes into the GoM/GB and the prominent role of riverine influxes and sedimentary denitrification in the MAB. However, detailed spatiotemporal analysis of nitrogen fluxes highlights the importance of dispersed inflows of shallow to intermediate depth waters (0–75 m), which can at times exceed the deep nitrogen influx emphasized in previous studies. A seasonal analysis shows a pronounced shift from the net import of nitrogen to the GoM/GB region during late fall and winter, to the net export of nitrogen from the region in the spring and early summer. The MAB, in contrast, consistently exports nitrogen to offshore waters. The prominence of the 0‐75m nitrogen supply has implications for the roles of Labrador Slope Water and Atlantic Temperate Slope Water on the NEUS ecosystems, as Atlantic Temperate Slope Water has greater nitrate concentrations than Labrador Slope Water at depth but often less at the surface. Results suggest the need for further study of shallow to intermediate depth inflows beyond those from the Scotian Shelf, particularly during the fall/winter of net nitrogen inflow.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".