Climate Change – Impact on the Sundarbans, a Case Study
Bibliographic record
Abstract
Sundarbans is the world’s largest contiguous mangrove forest and is a designated world heritage site. Shared by India and Bangladesh, it is home to several species including tigers. The habitat supports approximately 4.37 million people. As per the research conducted it is believed that the Sundarbans have soaked in 4.15 crore tonnes of carbon dioxide. Due to climate change the Sundarbans faces several challenges.With rising sea levels, islands are disappearing and the increasing salinity in the water and soil has severely threatened the health of mangrove forests and the quality of soil and crops. Additionally, there have been serious disturbances to hydrological parameters and change in fishing patterns, resulting in disastrous consequences for fishermen. Frequent cyclones and erratic monsoon raining pattern are damaging ecology and humanity. In addition to general environment protection laws, India has also set up institutes at both the Central and State levels to specifically tackle the effects of climate change on Sundarbans. However, split responsibilities between Centre and States and multitude of institutions has resulted in overlap of responsibilities, loss of time and resources, which makes the institutions ineffective. With risk of the Sundarbans submerging, there is an urgent need for global reduction of emissions and replacement of fossil fuels with renewable energy. Governments also need to promote plantation of local saline resistant seeds. Even as the State and Central Government of India finalise action plans to tackle the problems of climate change and take steps for poverty alleviation in one of the world’s poorest regions, there is a pressing requirement to set up flood relief centres and rapid action response teams to cyclones and storms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".