Nitrogen cycling in shallow low-oxygen coastal waters off Peru from nitrite and nitrate nitrogen and oxygen isotopes
Bibliographic record
Abstract
Abstract. O2 deficient zones (ODZs) of the world's oceans are important locations for microbial dissimilatory nitrate (NO3−) reduction and subsequent loss of combined nitrogen (N) to biogenic N2 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 NO3− and nitrite (NO2−) 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 NO2− concentrations were high, ranging from 6 to 10 µM, and NO3− was in places strongly depleted to near 0 µM. We observed for the first time a positive linear relationship between NO2−δ15N and δ18O at our coastal stations, analogous to that of NO3− N and O isotopes during NO3− uptake and dissimilatory reduction. This relationship is likely the result of rapid NO2− turnover due to higher organic matter flux in these coastal upwelling waters. No such relationship was observed at offshore stations where slower turnover of NO2− 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 δ15N of NO3−, dissolved inorganic N, or biogenic N2, 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 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 | 0.001 | 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 | 0.001 |
| 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".