Extreme Nitrate Deficits in the Western Arctic Ocean: Origin, Decadal Changes, and Implications for Denitrification on a Polar Marginal Shelf
Bibliographic record
Abstract
Abstract The western Arctic Ocean is known to be nitrate deficient relative to phosphate but the decadal trend and the processes contributing to the deficit are not clear. To investigate changes in this extreme nitrate deficit of over 10 μmol/kg and its causal mechanisms, nutrient concentrations were examined along a transect spanning the Bering Basin, the Bering–Chukchi Shelf, and the western Arctic Ocean Basin over the last two decades (1994–2018). The results show that the extreme nitrate deficit has extended to greater depths and further north during the past two decades, which coincided with the expansion of Pacific water in the western Arctic Ocean. Subsurface nutrient stocks in the basin areas appear to have increased, but are accompanied by a larger nitrate deficit, which may be due to stronger shelf denitrification. This nitrate loss (∆N) caused by shelf denitrification was estimated to be 7.3 ± 0.1 μmol/kg during the interval 2012–2018, which was ∼10% higher than that in 1994. This suggests an intensification of denitrification on this marginal shelf under climatic and environmental change in the Arctic Ocean.
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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 | 0.001 |
| 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".