Reconstruction of Pacific Ocean bottom water salinity during the Last Glacial Maximum
Bibliographic record
Abstract
Abstract Knowledge of salinity in the deep ocean is important for understanding past ocean circulation and climate. Based on sedimentary pore fluid chloride measurements of a single Pacific site, Adkins et al. ( ) suggested that, during the Last Glacial Maximum (LGM), the Pacific deep bottom water was saltier than expected based on lower sea level alone. Here we present high‐resolution salinity profiles from five sites in the South, Equatorial, and North Pacific Ocean. Our study greatly constrains understanding of LGM salinity in the Pacific Ocean. Our results show that LGM chloride concentrations of deep Pacific bottom water were 4.09 ± 0.4% greater than today's values. Pacific Ocean bottom water salinity was also indistinguishable from being homogeneous across the wide range of latitudes studied here. These LGM salinity reconstructions are on average slightly higher (~1.4 to 1% higher) than expected from sea level of the time, which is generally inferred to have been ~120 to ~135 m lower than today.
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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 | 0.001 |
| 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".