N2 production rates limited by nitrite availability in the Bay of Bengal oxygen minimum zone
Bibliographic record
Abstract
A third or more of the fixed nitrogen lost from the oceans as N2 is removed by anaerobic microbial processes in open ocean oxygen minimum zones. These zones have expanded over the past decades, and further anthropogenically induced expansion could accelerate nitrogen loss. However, in the Bay of Bengal there has been no indication of nitrogen loss, although oxygen levels are below the detection level of conventional methods (1 to 2 μM). Here we quantify the abundance of microbial genes associated with N2 production, measure nitrogen transformations in incubations of sampled seawater with isotopically labelled nitrogen compounds and analyse geochemical signatures of these processes in the water column. We find that the Bay of Bengal supports denitrifier and anammox microbial populations, mediating low, but significant N loss. Yet, unlike other oxygen minimum zones, our measurements using a highly sensitive oxygen sensor demonstrate that the Bay of Bengal has persistent concentrations of oxygen in the 10 to 200 nM range. We propose that this oxygen supports nitrite oxidation, thereby restricting the nitrite available for anammox or denitrification. If these traces of oxygen were removed, nitrogen loss in the Bay of Bengal oxygen minimum zone waters could accelerate to global significance. Nitrogen losses have not been observed in the Bay of Bengal, unlike in other ocean oxygen minimum zones. Chemical and molecular analyses reveal that trace levels of oxygen inhibit nitrate formation, largely preventing microbial N2 production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 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".