The influence of decadal oscillations on the oxygen and nutrienttrends in the Pacific Ocean
Bibliographic record
Abstract
Abstract. A strong oxygen deficient layer is located in the upper layer of the tropical Pacific Ocean and at deeper depths in the North Pacific. Processes related to climate change (upper ocean warming, reduced ventilation) are expected to change ocean oxygen and nutrient inventories. In most ocean basins, a decrease in oxygen (‘deoxygenation’) and an increase of nutrients has been observed in subsurface layers. Deoxygenation trends are not linear and there could be other influences on oxygen and nutrient trends and variability. Here oxygen and nutrient time series since 1950 in the Pacific Ocean were investigated at 50 to 300 m depth, as this layer provides critical pelagic habitat for biological communities. In addition to trends related to ocean warming the oxygen and nutrient trends show a strong influence of the Pacific Decadal Oscillation (PDO) in the tropical and the eastern Pacific, and the North Pacific Gyre Oscillation (NPGO) especially in the North Pacific. In the Oyashio Region the PDO, the NPGO, the North Pacific Index (NPI) and a 18.6 year nodal tidal cycle overlay the long-term trend. In most regions oxygen increases and nutrients decrease in the 50 to 300 m layer during the negative PDO phase, with opposite trends during the positive PDO phase. The PDO index encapsulates the major mode of surface temperature variability in the Pacific and oxygen and nutrients trends throughout the basin can be described in the context of the PDO phases. An influence of the subtropical-tropical cell in the tropical Pacific cannot be proven with the available data. El Niño and La Niña years often influence the oxygen and nutrient distribution during the event in the eastern tropical Pacific, but do not have a multi-year influence on the trends.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".