Analysis of Indian monsoon daily rainfall on subseasonal to multidecadal time‐scales using a hidden Markov model
Bibliographic record
Abstract
Abstract A 70‐year record of daily monsoon‐season rainfall at a network of 13 stations in central western India is analyzed using a 4‐state homogeneous hidden Markov model. The diagnosed states are seen to play distinct roles in the seasonal march of the monsoon, can be associated with ‘active’ and ‘break’ monsoon phases and capture the northward propagation of convective disturbances associated with the intraseasonal oscillation. Interannual variations in station rainfall are found to be associated with the alternation, from year to year, in the frequency of occurrence of wet and dry states; this mode of variability is well correlated with both all‐India monsoon rainfall and an index characterizing the strength of the El Niño Southern Oscillation. Analysis of low‐passed time series suggests that variations in state frequency are responsible for the modulation of monsoon rainfall on multidecadal time‐scales as well. Copyright © 2008 Royal Meteorological Society
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".