Role of sediment denitrification in water column oxygen dynamics: comparison of the North American East and West Coasts
Bibliographic record
Abstract
Abstract. Low oxygen concentrations, either natural or anthropogenically driven, can severely affect coastal marine ecosystems. A deeper understanding of oxygen dynamics is required in order to improve numerical models, eventually to predict the timing and severity of hypoxia. In this study we investigate the effect of sediment denitrification on oxygen concentrations in bottom waters over the continental shelf. We used two coupled physical-biological models based on the Regional Ocean Modelling System (ROMS) to compare summer simulations with and without denitrification within the sediments for two North American shelves: the Middle Atlantic Bight (MAB) and the Vancouver Island Shelf (VIS). These regions belong to western and eastern boundary current systems, respectively, and are characterized by different physical and biological dynamics. Both models assume coupled nitrification-denitrification within the sediments. Denitrification represents a loss of bioavailable nitrogen through the production of dinitrogen gas, with the potential to affect biogeochemical cycles. In our MAB model, this loss of regenerated nutrients through denitrification within the sediments significantly affects primary production, since recycled nitrogen supports most of the primary production in that region. The diminished primary production and consequent decrease of organic matter flux to the seafloor leads to less sediment oxygen consumption and higher oxygen concentrations in bottom waters. However, changes in regenerated nitrogen on the VIS barely affect primary production due to the efficient supply of new nutrients through wind-driven upwelling during summer and the nutrient-rich coastal current. We recommend that modelling experiments focusing on oxygen dynamics (as well as oxygen budget calculations) should include sediment denitrification in coastal regions where regenerated primary production dominates productivity.
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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".