ΔO2/N2′ as a New Tracer of Marine Net Community Production: Application and Evaluation in the Subarctic Northeast Pacific and Canadian Arctic Ocean
Bibliographic record
Abstract
We compared field measurements of the biological O2 saturation anomalies, ΔO2/Ar and ΔO2/N2, from simultaneous oceanographic deployments of a membrane inlet mass spectrometer and optode/gas tension device (GTD). Data from the Subarctic Northeast Pacific and Canadian Arctic Ocean were used to evaluate ΔO2/N2 as an alternative to ΔO2/Ar for estimates of mixed layer net community production (NCP). We observed strong spatial coherence between ΔO2/Ar and ΔO2/N2, with small offsets resulting from differences in the solubility properties of Ar and N2 and their sensitivity to vertical mixing fluxes. Larger offsets between the two tracers were observed across hydrographic fronts and under elevated sea states, resulting from the differential time-response of the optode and GTD, and from bubble dissolution in the ship’s seawater lines. We used a simple numerical framework to correct for physical sources of divergence between N2 and Ar, deriving the tracer ΔO2/N2′. Over most of our survey regions, ΔO2/N2′ provided a better analog for ΔO2/Ar, and thus more accurate NCP estimates than ΔO2/N2. However, in coastal Arctic waters, ΔO2/N2 and ΔO2/N2′ performed equally well as NCP tracers. On average, mixed layer NCP estimated from ΔO2/Ar and ΔO2/N2′ agreed to within ∼2 mmol O2 m–2 d–1, with offsets typically smaller than other errors in NCP calculations. Our results demonstrate a significant potential to derive NCP from underway O2/N2 measurements across various oceanic regions. Optode/GTD systems could replace mass spectrometers for autonomous NCP derivation under many oceanographic conditions, thereby presenting opportunities to significantly expand global NCP coverage from various underway platforms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".