What are the drivers of Caspian Sea level variation during the late Quaternary?
Bibliographic record
Abstract
Quaternary Caspian Sea level variations depended on geophysical processes (affecting the opening and closing of gateways and basin size/shape) and hydro-climatological processes (affecting water balance). Disentangling the drivers of past Caspian Sea level variation, as well as the mechanisms by which they impacted the Caspian Sea level variation, is much debated. In this study we examine the relative impacts of hydroclimatic change, ice-sheet accumulation and melt, and isostatic adjustment on Caspian Sea level change. We performed model analysis of ice-sheet and hydroclimate impacts on Caspian Sea level and compared these with newly collated published palaeo-Caspian sea level data for the last glacial cycle. We used palaeoclimate model simulations from a global coupled ocean-atmosphere-vegetation climate model, HadCM3, and ice-sheet data from the ICE-6G_C glacial isostatic adjustment model. Our results show that ice-sheet meltwater during the last glacial cycle played a vital role in Caspian Sea level variations, which is in agreement with hypotheses based on palaeo-Caspian Sea level information. The effect was directly linked to the reorganization and expansion of the Caspian Sea palaeo-drainage system resulting from topographic change. The combined contributions from meltwater and runoff from the expanded basin area were primary factors in the Caspian Sea transgression during the deglaciation period between 20 and 15 kyr BP. Their impact on the evolution of Caspian Sea level lasted until around 13 kyr BP. Millennial scale events (Heinrich events and the Younger Dryas) negatively impacted the surface water budget of the Caspian Sea but their influence on Caspian Sea level variation was short-lived and was outweighed by the massive combined meltwater and runoff contribution over the expanded basin.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".