A Brief Account on the Impacts of Climate Change, Pollution and Coastal Erosion in the Coastal Districts of Odisha
Bibliographic record
Abstract
AbstractOdisha, being the coastal region situated in the eastern part of India, has a coastline that is stretched to 480 KM long and is vulnerable to climate change, pollution, and erosion, posing a threat to surrounding biodiversity, communities, and ecosystems. Coastal erosion is accelerated due to heavy rainfall and extreme events like cyclones and storm surges, whereas saltwater intrusion degrades the freshwater bodies and agricultural land. Pollution caused by anthropogenic activities such as industrial discharge, tourism activities, and single-use plastic waste disrupts the coastal and marine ecosystem. These changes alter the livelihood opportunities and tourism-dependent economies, causing threats to critical species. Combining initiatives by the government and public participation has accelerated community-based adaptation methods through the restoration of mangroves, implementing early warning systems, and enhancing sustainable practices to increase the resilience of the state. Though long-term sustainability is encouraged by creating a stronger framework, pollution-related awareness, and engagement of stakeholders, decision-makers, as well as communities, to curb the impacts of climate change. This paper analyses various challenges, including climate change, pollution, and coastal erosion in the coastal regions of Odisha, exploring the adaptation and mitigation strategies for a sustainable and climate-resilient future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".