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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".