Environmental Impacts of Ship Breaking and Recycling Industry of Sitakunda, Chittagong, Bangladesh
Bibliographic record
Abstract
Ship dismantling of Bangladesh is a reason of concern due to its economic values and environmental hazards. This study focuses on the Ship Breaking and Recycling Industry (SBRI) of Bangladesh to assess the environmental impacts. It was done by analyzing the water quality parameters like, Turbidity, Salinity, Electric conductivity (EC), Dissolved Oxygen (DO), pH, Total Dissolved Solids (TDS), Temperature, Ammonia-Nitrogen, Nitrate–Nitrogen, Phosphate concentrations of inside and surroundings of Ship Breaking Yard. Turbidity ranged from 7.71 to 119 FTU in and 4.07 to 41.74 FTU in inside and outside the ship breaking yard, respectively. Salinity ranged from 0.95 to 14.28 ppt and 0.06 to 0.79 ppt in insde and outside, respectively. Value of EC varied from1.75 to 1280 µs/cm in inside and 0.21 to 15.7 µs/cm in outside, DO ranged from 3.77 to 7.94 mg/l in inside and 1.95 to 5.34 mg/l in outside. TDS value ranged from1280 to 15340 mg/l in inside and 74 to 825 mg/l in outside of ship breaking yard. Ammonia-N value ranged from 0.46 to 7.046 mg/l, Nitrate-N value ranged from 0.10 to 6.9 mg/l and Phosphate value ranged from 0.175 to 4.75 mg/l in the analyzed water sample. Magnitude of environmental alteration by establishment of ship breaking industry was assessed by quantifying Environmental impact value of Study area and the value was found as -93, where ecological parameters value was found -72, Physico-chemical parameters value was -70 and human interest value was found +49 which indicate negative alteration on environment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".