N<sub>2</sub>O production and consumption from stable isotopic and concentration data in the Peruvian coastal upwelling system
Bibliographic record
Abstract
Abstract The ocean is an important source of nitrous oxide (N 2 O) to the atmosphere, yet the factors controlling N 2 O production and consumption in oceanic environments are still not understood nor constrained. We measured N 2 O concentrations and isotopomer ratios, as well as O 2 , nutrient and biogenic N 2 concentrations, and the isotopic compositions of nitrate and nitrite at several coastal stations during two cruises off the Peru coast (~5–16°S, 75–81°W) in December 2012 and January 2013. N 2 O concentrations varied from below equilibrium values in the oxygen deficient zone (ODZ) to up to 190 nmol L −1 in surface waters. We used a 3‐D‐reaction‐advection‐diffusion model to evaluate the rates and modes of N 2 O production in oxic waters and rates of N 2 O consumption versus production by denitrification in the ODZ. Intramolecular site preference in N 2 O isotopomer was relatively low in surface waters (generally −3 to 14‰) and together with modeling results, confirmed the dominance of nitrifier‐denitrification or incomplete denitrifier‐denitrification, corresponding to an efflux of up to 0.6 Tg N yr −1 off the Peru coast. Other evidence, e.g., the absence of a relationship between ΔN 2 O and apparent O 2 utilization and significant relationships between nitrate, a substrate during denitrification, and N 2 O isotopes, suggest that N 2 O production by incomplete denitrification or nitrifier‐denitrification decoupled from aerobic organic matter remineralization are likely pathways for extreme N 2 O accumulation in newly upwelled surface waters. We observed imbalances between N 2 O production and consumption in the ODZ, with the modeled proportion of N 2 O consumption relative to production generally increasing with biogenic N 2 . However, N 2 O production appeared to occur even where there was high N loss at the shallowest stations.
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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".