Bibliographic record
Abstract
Abstract. Understanding the response of the ocean to global warming, including the renewal of ocean waters from the surface (ventilation), is important for future climate predictions. Oxygen (O2) distributions in the ocean thermocline have proven an effective way to infer changes in ventilation because physical processes (ventilation and circulation) that supply oxygen are thought to be primarily responsible for changes in interior oxygen concentrations. Here, the focus is on the North Pacific thermocline, where some of the world ocean's largest oxygen variations have been observed. These variations, described as bi-decadal cycles on top of a small declining trend, are strongest on subsurface isopycnals that outcrop into the mixed layer of the northwestern North Pacific in late winter. In this study, surface density time series are reconstructed in this area using observational data only and focusing on the time period from 1982, the first full year of the satellite SST record, to 2020. It is found that changes in maximum annual outcrop area of the densest isopycnals outcropping in the northwestern North Pacific are correlated with interannual oxygen variability observed at Ocean Station P (OSP) downstream at about a 10-year lag. The hypothesis is that ocean ventilation/uptake of oxygen is greatly reduced when the outcrop areas are small and that this signal travels within the North Pacific Current to OSP, with 10 years being at the higher end of transit times reported in other studies. It is also found that sea surface salinity (SSS) dominates over sea surface temperature (SST) in driving interannual fluctuations in maximum annual surface density in the northwestern North Pacific, highlighting the role that salinity may play in altering ocean ventilation. In contrast, SSS and SST contribute about equally to the long-term declining surface density trends that are superimposed on the interannual cycles.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.276 | 0.190 |
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".