Data‐based estimates of suboxia, denitrification, and N<sub>2</sub>O production in the ocean and their sensitivities to dissolved O<sub>2</sub>
Why this work is in the frame
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Bibliographic record
Abstract
Oxygen minimum zones (OMZs) are major sites of fixed nitrogen removal from the open ocean. However, commonly used gridded data sets such as the World Ocean Atlas (WOA) tend to overestimate the concentration of O 2 compared to measurements in grids where O 2 falls in the suboxic range (O 2 < 2–10 mmol m −3 ), thereby underestimating the extent of O 2 depletion in OMZs. We evaluate the distribution of the OMZs by (1) mapping high‐quality oxygen measurements from the WOCE program, and (2) by applying an empirical correction to the gridded WOA based on in situ observations. The resulting suboxic volumes are a factor 3 larger than in the uncorrected gridded WOA. We combine the new oxygen data sets with estimates of global export and simple models of remineralization to estimate global denitrification and N 2 O production. We obtain a removal of fixed nitrogen of 70 ± 50 Tg year −1 in the open ocean and 198 ± 64 Tg year −1 in the sediments, and a global N 2 O production of 6.2 ± 3.2 Tg year −1 . Our results (1) reconcile water column denitrification rates based on global oxygen distributions with previous estimates based on nitrogen isotopes, (2) revise existing estimates of sediment denitrification down by 1/3 d through the use of spatially explicit fluxes, and (3) provide independent evidence supporting the idea of a historically balanced oceanic nitrogen cycle. These estimates are most sensitive to uncertainties in the global export production, the oxygen threshold for suboxic processes, and the efficiency of particle respiration under suboxic conditions. Ocean deoxygenation, an expected response to anthropogenic climate change, could increase denitrification by 14 Tg year −1 of nitrogen per 1 mmol m −3 of oxygen reduction if uniformly distributed, while leaving N 2 O production relatively unchanged.
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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 | 0.000 |
| 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 it