Assessment of Environmental Aspects and Impacts of Scientific Laboratories of a University: Focus on Gap Analysis and Environmental Management System (EMS) Implementation
Bibliographic record
Abstract
Environmental Management System (EMS) has become an important tool for organizations looking towards managing their environmental issues such as pollution, legal compliance and minimizing their environmental impacts. The present study was conducted to assess the environmental aspects and impact of selected scientific laboratories of Jahangirnagar University in Bangladesh with focus on the gap analysis for implementing EMS. Data and information were collected through frequent laboratory visits, focus group discussion, questionnaire survey and key informant interview. It was found that EMS was not implemented in the laboratories and the staffs and researchers of the university had very limited idea about EMS. Surface water, air and soil pollution; unsafe mixing and handling of hazardous materials and chemicals; unsustainable storage of chemicals and reagents; improper use of personal protective equipment found as the main environmental challenges in these laboratories. The maximum negative environmental impact occurred in the chemistry and botany laboratories, as large number of researchers here used high amount of chemicals and cultured media, while the minimum pollution was found in microbiology and environmental sciences laboratories. Although, the overall pollution levels were low, there were lots of gaps in introducing EMS. Therefore, initiatives should be taken. Jahangirnagar University Environmental Bulletin, Vol.2, 9-17, 2013 DOI: http://dx.doi.org/10.3329/jueb.v2i0.16325
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".