Oxygen utilization rate (OUR) underestimates ocean respiration: A model study
Bibliographic record
Abstract
Abstract We use a simple 1‐D model representing an isolated density surface in the ocean and 3‐D global ocean biogeochemical models to evaluate the concept of computing the subsurface oceanic oxygen utilization rate (OUR) from the changes of apparent oxygen utilization (AOU) and water age. The distribution of AOU in the ocean is not only the imprint of respiration in the ocean's interior but is strongly influenced by transport processes and eventually loss at the ocean surface. Since AOU and water age are subject to advection and diffusive mixing, it is only when they are affected both in the same way that OUR represents the correct rate of oxygen consumption. This is the case only when advection prevails or with uniform respiration rates, when the proportions of AOU and age are not changed by transport. In experiments with the 1‐D tube model, OUR underestimates respiration when maximum respiration rates occur near the outcrops of isopycnals and overestimates when maxima occur far from the outcrops. Given the distribution of respiration in the ocean, i.e., elevated rates near high‐latitude outcrops of isopycnals and low rates below the oligotrophic gyres, underestimates are the rule. Integrating these effects globally in three coupled ocean biogeochemical and circulation models, we find that AOU‐over‐age based calculations underestimate true model respiration by a factor of 3. Most of this difference is observed in the upper 1000 m of the ocean with the discrepancies increasing toward the surface where OUR underestimates respiration by as much as factor of 4.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".