Red Sea during the Last Glacial Maximum: Implications for sea level reconstruction
Bibliographic record
Abstract
The Red Sea is connected to the Indian Ocean via a narrow and shallow strait and exhibits a high sensitivity to atmospheric changes and a reduced sea level. We used an ocean general circulation model to investigate the hydrography and circulation in the Red Sea in response to reduced sea level and modified atmospheric conditions occurring during the Last Glacial Maximum (LGM). The model salinity shows high sensitivity to sea level reduction together with a mild atmospheric impact. Sea level reduction affects the stratification and alters the circulation pattern at the Strait of Bab el Mandab, which experiences a transition from a submaximal flow to a maximal flow. The best correlation to reconstructed conditions during LGM exists when the water depth of the Hanish Sill (the shallowest part in the Strait of Bab el Mandab) is 33 ± 10.75 m, which would be affected by a sea level lowering of approximately 105 m. Our results support the reconstructed maximum salinity of around 57 practical salinity units because of a simple model (that takes into account mixing processes along the strait) and comparison of the surface salinity gradient to reconstructions based on isotopic records from sedimentary cores. The salinity and δ18O are sensitive to the mixing process at the strait, and the sensitivity increases as the sea level is further reduced. A local relative sea level reduction of approximately 105 m is also in close agreement with the inference of the LGM low stand of the sea at the location of the sill based on the ICE‐5G (VM2) model.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".