On the relationship between Tibetan snow cover, the Tibetan plateau monsoon and the Indian summer monsoon
Bibliographic record
Abstract
More than a century ago, Blanford suggested that an inverse relationship existed between summer rainfall over Northwest India and snow cover in the western Himalaya. Recently it has been found that there is a positive correlation between Tibetan snow cover and Indian summer monsoon rainfall (IMR), a result opposite to that of Blanford. In this paper, we attempt to reconcile these contradictory observations through the analysis of spatial and temporal variability of Tibetan snow cover and its relationship with the Indian summer monsoon. We show that there exists an east‐west dipole‐like correlation pattern between snow cover over the Tibetan plateau and IMR that underwent a change in sign around 1985. We argue that variability in the Tibetan plateau monsoon is responsible for the spatial and temporal variability in the relationship between Tibetan snow cover and the Indian summer monsoon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".