New Deoxygenation Threshold for N<sub>2</sub> and N<sub>2</sub>O Production in Coastal Waters and Sediments
Bibliographic record
Abstract
Abstract Bioavailable nitrogen governs ocean productivity and carbon fixation by regulating phytoplankton growth and community composition. Nitrogen input primarily results from fixation, while denitrification and anammox remove bioavailable nitrogen in oxygen‐depleted conditions. Traditionally considered limited to highly suboxic (i.e., <5 μM) waters, recent studies suggest that fixed‐nitrogen removal processes may extend beyond, elevating global nitrogen loss estimates. This study directly quantifies fixed‐nitrogen loss across oxygen gradients (from 140 to 32 μM) along the Estuary and Gulf of St. Lawrence using N cycle tracers (, , and ). Notably, we observe significant production when ambient concentrations fall below a threshold value of 58.9 ± 1.1 μM, including potential water column fixed‐nitrogen removal processes above suboxia. We hypothesis that ambient deoxygenation eases the formation of suboxic microareas in suspended organic matter. Benthic production remains unaffected under intensifying water column deoxygenation from 50 down to 32 μM, but the contribution of produced through nitrification in the sediment to denitrification diminishes as deoxygenation intensifies. Combined, water column and benthic fixed‐nitrogen removal processes drive anomalies and strong deficiency in bottom waters. Additionally, the observed threshold also triggers production. Overall, our study highlights the profound impact of coastal ocean deoxygenation on nitrogen cycling, suggesting unexpected shifts even at ambient oxygen concentrations traditionally considered well above suboxic conditions.
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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".