Global Warming and Its Effect on Flow in Ganga River
Bibliographic record
Abstract
In this work, water flow in the Ganga River has been analyzed based on the available data starting  from the year 1850 to the average of years 2002 to 2006 at Farakka in West Bengal. The flow has been related  with rainfall data in the Ganga basin starting in the year 1980, and the average rainfall data in the years 2002 to  2006. The year 1980 has been chosen based on the fact that after this year the global warming trend has increased  (Adhikari and Huybrechts, 2009). The location at Farakka has been chosen to incorporate the contributions of  entire Himalayan glaciers in enhancing the flow of Ganga upstream of Farakka. This way, the results have been  presented to include the effects of glaciers melting at the source of Ganga and also the contributions of other rivers  which are tributaries of Ganga. The result shows that there is significant effect of global warming on melting of  Himalayan glaciers and the flow rate of Ganga.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".