Biological and physical controls on N<sub>2</sub>, O<sub>2</sub>, and CO<sub>2</sub> distributions in contrasting Southern Ocean surface waters
Bibliographic record
Abstract
Abstract We present measurements of pCO2, O2 concentration, biological oxygen saturation (ΔO2/Ar), and N2 saturation (ΔN2) in Southern Ocean surface waters during austral summer, 2010–2011. Phytoplankton biomass varied strongly across distinct hydrographic zones, with high chlorophyll a (Chl a) concentrations in regions of frontal mixing and sea ice melt. pCO2 and ΔO2/Ar exhibited large spatial gradients (range 90 to 450 µatm and −10 to 60%, respectively) and covaried strongly with Chl a. However, the ratio of biological O2 accumulation to dissolved inorganic carbon (DIC) drawdown was significantly lower than expected from photosynthetic stoichiometry, reflecting the differential time scales of O2 and CO2 air‐sea equilibration. We measured significant oceanic CO2 uptake, with a mean air‐sea flux (~ −10 mmol m−2 d−1) that significantly exceeded regional climatological values. N2 was mostly supersaturated in surface waters (mean ΔN2 of +2.5%), while physical processes resulted in both supersaturation and undersaturation of mixed layer O2 (mean ΔO2phys = 2.1%). Box model calculations were able to reproduce much of the spatial variability of ΔN2 and ΔO2phys along the cruise track, demonstrating significant effects of air‐sea exchange processes (e.g., atmospheric pressure changes and bubble injection) and mixed layer entrainment on surface gas disequilibria. Net community production (NCP) derived from entrainment‐corrected surface ΔO2/Ar data, ranged from ~ −40 to > 300 mmol O2 m−2 d−1 and showed good coherence with independent NCP estimates based on seasonal mixed layer DIC deficits. Elevated NCP was observed in hydrographic frontal zones and stratified regions of sea ice melt, reflecting physical controls on surface water light fields and nutrient availability.
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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) | 0.001 | 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".