Nitrogen cycling in shallow low-oxygen coastal waters off Peru from nitrite and nitrate nitrogen and oxygen isotopes
Bibliographic record
Abstract
O 2 deficient zones (ODZs) of the world's oceans are important locations for microbial dissimilatory nitrate (NO 3 − ) reduction and subsequent loss of combined nitrogen (N) to biogenic N 2 gas. ODZs are generally coupled to regions of high productivity leading to high rates of N-loss as found in the coastal upwelling region off Peru. Stable N and O isotope ratios can be used as natural tracers of ODZ N-cycling because of distinct kinetic isotope effects associated with microbially mediated N-cycle transformations. Here we present NO 3 − and nitrite (NO 2 − ) stable isotope data from the nearshore upwelling region off Callao, Peru. Subsurface oxygen was generally depleted below about 30 m depth with concentrations less than 10 µM, while NO 2 − concentrations were high, ranging from 6 to 10 µM, and NO 3 − was in places strongly depleted to near 0 µM. We observed for the first time a positive linear relationship between NO 2 − δ 15 N and δ 18 O at our coastal stations, analogous to that of NO 3 − N and O isotopes during NO 3 − uptake and dissimilatory reduction. This relationship is likely the result of rapid NO 2 − turnover due to higher organic matter flux in these coastal upwelling waters. No such relationship was observed at offshore stations where slower turnover of NO 2 − facilitates dominance of isotope exchange with water. We also evaluate the overall isotope fractionation effect for N-loss in this system using several approaches that vary in their underlying assumptions. While there are differences in apparent fractionation factor ( ε ) for N-loss as calculated from the δ 15 N of NO 3 − , dissolved inorganic N, or biogenic N 2 , values for ε are generally much lower than previously reported, reaching as low as 6.5 ‰. A possible explanation is the influence of sedimentary N-loss at our inshore stations which incurs highly suppressed isotope fractionation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".