MEASUREMENT OF LEAD CONCENTRATION IN THE BLOOD OF PUBLIC TRANSPORT DRIVERS IN BANDUNG REGENCY, WEST JAVA, INDONESIA
Bibliographic record
Abstract
As industrial and transportation activities in Bandung Regency are growing rapidly, Indonesia could be at risk of increasing air pollution levels. One of the air pollutants that are very harmful to the body is lead (Pb) generated from industrial activities, mining, vehicle exhaust gas, and dust from the ground. Lead is a heavy metal that is very dangerous for the body because it is carcinogenic with its activity character as an inhibitor in cell metabolism. This study aimed to analyze the concentration of lead in the blood of public transport drivers who are active on the highway every day and are most at risk of being exposed to Pb. Measurement of Pb concentration was carried out using an Atomic Absorption Spectrophotometer (AAS). Blood samples were taken from public transport drivers at Soreang Terminal, Bandung Regency, West Java, Indonesia. The analysis results showed that the average blood lead content of public transport drivers was 1,032 mg/L. The lowest level was 0.889 mg/L, and the highest was 1,200 mg/L. This shows that the lead content in the blood of public transport drivers is already in excess levels (range numbers 0.800-1.200 mg/L) when compared with the threshold for lead in the blood based on the Regulation of the Ministry of Health of the Republic of Indonesia (0.10 - 0.25 mg/L) and the threshold value set by the World Health Organization, which is 0.4 mg/L.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".