Accurate Estimation of Net Community Production From O<sub>2</sub>/Ar Measurements
Bibliographic record
Abstract
Under physically isolated conditions, net community production (NCP) can be accurately estimated from the rate of oxygen evasion to the atmosphere derived from local mixed layer oxygen/argon measurements. We use a simple box model to demonstrate that, when physical inputs are negligible, the sea‐to‐air flux of biological oxygen (bioflux) represents the average NCP exponentially weighted over the past several residence times of oxygen in the mixed layer. This new weighting scheme shows that there is no apparent lag between bioflux and exponentially weighted time‐averaged NCP. Furthermore, a strict steady state assumption is unnecessary to this relationship. However, this widely used O2/Ar method is not effective in dynamic coastal zones where low oxygen water upwells to the surface. Yet these zones are highly productive and their episodic productivity needs to be quantified. We use a quasi‐2‐D version of the Regional Ocean Modeling System, including oxygen and argon as prognostic variables, to explore the application of this method and the relationship between NCP and bioflux in a coastal upwelling system. We show that bioflux is an accurate measure of NCP over large regions of time and space. Bioflux is most biased near the shore following upwelling favorable winds, where bioflux is sometimes negative (flux from the atmosphere to the ocean) and even positive bioflux values can severely underestimate NCP. Assessing a range of model variables that are easily observed in the field, we show that sea surface temperature is the most effective at identifying bioflux measurements that are likely to be biased.
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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 | 0.002 |
| 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 | 0.001 |
| 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".