Isopycnal Shoaling Causes Interannual Variability in Oxygen on Isopycnals in the Subarctic Northeast Pacific
Bibliographic record
Abstract
Abstract Over 60 years of oceanographic observations from Ocean Station Papa (OSP) in the northeast Pacific indicate faster dissolved oxygen loss than the global average. The greatest negative trends in oxygen concentration occur on isopycnals in the upper water column (σθ = 26.1–26.8 kg m−3, ∼110–200 m) but have considerable uncertainty due to natural variability. In this paper, we use eight Argo profiling floats equipped with optode oxygen sensors to assess the 2008–2016 interannual variability of subsurface dissolved oxygen near OSP. We developed a method using high frequency Conductivity‐Temperature‐Depth data to correct optode profiles for slow response times and used reference profiles from the OSP time series to calibrate the optodes. Response time correction markedly improves subsurface bias caused by slow optode equilibration. Our analysis indicates that episodic shoaling of isopycnals can cause rapid reduction in dissolved oxygen concentration. Changes in ventilation, horizontal mixing, and water mass age are unlikely drivers for the rapid O2 loss events examined. We link dissolved oxygen loss during shoaling events to organic matter export, due to higher concentrations of organic matter and greater respiration rates at shallower depths. Reduced net community production during the “Blob” marine heatwave may have reduced the impact of the second shoaling event examined. Natural variations in dissolved oxygen in these layers provide context for uncertainty estimates of long‐term trends and insight toward the potential for future extreme oxygen minima from the combined impact of the long‐term decline and episodic shoaling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".