Fixed-nitrogen loss in a deoxygenating coastal ocean: Insights from the Estuary and Gulf of St. Lawrence
Bibliographic record
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 removes bioavailable nitrogen in oxygen-depleted conditions. Traditionally considered limited to highly suboxic (i.e., < 5 µM) waters, recent studies suggest 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 35 µM) along the Estuary and Gulf of St. Lawrence using N cycle tracers (, and ). Notably, we observe significant production when concentrations fall below 57-52 µM, including unexpected water column fixed-nitrogen removal processes above suboxia. Benthic production remains unaffected under intensifying deoxygenation from 50 down to 34 µM, but sedimentary nitrification contribution to denitrification diminishes with intensifying deoxygenation. Combined, water column and benthic fixed-nitrogen removal processes drive anomalies and strong deficiency in bottom waters. Additionally, observed concentration threshold triggers production, unveiling the profound impact of ocean deoxygenation on nitrogen cycling, challenging conventional expectations even at hypoxic concentrations.
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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 | 0.000 |
| 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 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".