Status and Impacts of Industrial Pollution on the Karnafully River in Bangladesh: A Review
Bibliographic record
Abstract
Rapid growth of urbanization and industrialization in Bangladesh has been coupled with increasing environmental pollution. The coastal and estuarine ecosystems of the country are now facing increasing pollution pressures because of the elevated level of waste discharges from various sources. Major sources of pollution include domestic sewage, industrial waste, commercial waste, agricultural waste, institutional waste, street sweepings, construction debris, mining activities and sanitation residues etc. In this review, status and effect of solid waste pollution, heavy metal pollution, organochlorine pesticides pollution and oil pollution along with the Karnafully River Estuary is assessed by a comprehensive review, recorded by researchers especially on water, sediment and aquatic biota. Different study show that metal concentrations in estuarine water relatively higher due to rapid acceleration of industrial sector. Metal concentrations is higher in fish than water and sediment. Elevated level of trace metals is highly detrimental for fish and human mechanism shown by different studies. Oil pollution is responsible for environmental deterioration due to its adverse effects on estuarine biota, fish and shellfishes, phytoplankton and zooplankton. Industrialization is needed for the development of the country. But it should be eco-friendly for the effective and sustainable development and for the protection of the environment (aquatic).
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".