Bibliographic record
Abstract
Through the late Quaternary, the global climate system ranged from full glacial to temperate interglacial conditions. On a smaller spatial scale, regional climates of the late Quaternary exhibited fluctuations that were at times asynchronous to these global changes. For example, glacier expansion in the Himalayas during the mid‐Holocene appears to be at odds with the notion of increased global temperature. A clear understanding of the dynamical processes governing regional climate is therefore essential to the correct interpretation of proxy climate data. We summarize results from numerical simulations of the Last Glacial Maximum (LGM) and the mid‐Holocene, and focus on the multiple processes that control regional climate of the Himalaya and surrounding areas, with emphasis on monsoon dynamics and variability. It is shown that changes in the south Asian monsoon (caused by fluctuations in Earth's orbital parameters, by tropical Pacific Ocean temperatures, or by exposure of the Sunda shelf) alter the hydrological balance in regions bordering the Tibetan Plateau, a balance for which there are extensive continental proxy records. Numerical results correlate with the expansion/contraction cycles of deserts near the Chinese Loess Plateau. In addition, the LGM monsoon exhibits significant snow accumulation in the eastern Himalaya, whereas the mid‐Holocene monsoon exhibits increased accumulation in the northwestern Himalaya. Simulated changes are therefore in accord with field data and demonstrate that numerical simulations can be a useful tool in the interpretation of regional proxy data, particularly when those data are asynchronous to global records.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".