Ammonium and nitrite oxidation at nanomolar oxygen concentrations in oxygen minimum zone waters
Bibliographic record
Abstract
A major percentage of fixed nitrogen (N) loss in the oceans occurs within nitrite-rich oxygen minimum zones (OMZs) via denitrification and anammox. It remains unclear to what extent ammonium and nitrite oxidation co-occur, either supplying or competing for substrates involved in nitrogen loss in the OMZ core. Assessment of the oxygen (O2) sensitivity of these processes down to the O2 concentrations present in the OMZ core (<10 nmol⋅L(-1)) is therefore essential for understanding and modeling nitrogen loss in OMZs. We determined rates of ammonium and nitrite oxidation in the seasonal OMZ off Concepcion, Chile at manipulated O2 levels between 5 nmol⋅L(-1) and 20 μmol⋅L(-1) Rates of both processes were detectable in the low nanomolar range (5-33 nmol⋅L(-1) O2), but demonstrated a strong dependence on O2 concentrations with apparent half-saturation constants (Kms) of 333 ± 130 nmol⋅L(-1) O2 for ammonium oxidation and 778 ± 168 nmol⋅L(-1) O2 for nitrite oxidation assuming one-component Michaelis-Menten kinetics. Nitrite oxidation rates, however, were better described with a two-component Michaelis-Menten model, indicating a high-affinity component with a Km of just a few nanomolar. As the communities of ammonium and nitrite oxidizers were similar to other OMZs, these kinetics should apply across OMZ systems. The high O2 affinities imply that ammonium and nitrite oxidation can occur within the OMZ core whenever O2 is supplied, for example, by episodic intrusions. These processes therefore compete with anammox and denitrification for ammonium and nitrite, thereby exerting an important control over nitrogen loss.
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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 | 0.000 |
| 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 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".