Occupational Health Risk Assessment of Microbiological Air Quality in Buhemba Small Scale Gold Mines, Tanzania
Bibliographic record
Abstract
A large part of bio-aerosols is composed of biological particles such as bacteria, fungi, viruses, pollens, and their by-products such as endotoxins, metabolites, toxins, and other microbial fragments. These micro-organisms may affect human health with a wide range of adverse health effects including respiratory infections, allergies or toxic response in some individuals, especially susceptible ones. A cross-sectional study was conducted among purposely selected mining pits of Buhemba gold mine in Mara, Tanzania. To determine the microbial count, an agar strip loaded RCS® Microbial Air Sampler was used. Samples were collected at different microenvironments of the mining pit at a flow rate of 100 L min–1 for 5 minutes to yield a sample volume of 500 liters. Morphological characterization of both bacterial and fungal colonies was carried out followed by microscopic examination of fungal and gram-stained bacterial colonies. Regression analysis between mean bacterial and fungal spore counts with environmental factors like temperature and relative humidity was performed. The total aerobic bacteria concentration varied significantly (p value<0.05) between sampled pits (n=15), with the highest and lowest values of 3859 CFU/m3 and 1309 CFU/m3, respectively. However, there was no significant difference in total anaerobic bacteria concentration between sampled pits. The highest and lowest values of anaerobic bacteria were respectively 4284 CFU/m3 and 1190 CFU/m3. The highest and lowest values of total fungi concentration were 3314 CFU/m3 and 646 CFU/m3, respectively. High bacteria load that exceeds 1000 CFU/m3 as recommended by WHO was found in Buhemba gold mine.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".