Longitudinal study of pulmonary function trends and associated risk factors in iron ore miners
Bibliographic record
Abstract
The mining industry involves various processes, including extraction, crushing, milling, stacking, and reclaiming mineral substances. Workers in these environments are at risk of respiratory diseases due to exposure to high concentrations of pollutants, such as respirable dust. This study was conducted on sweepers, supervisors, and office workers of an iron ore Concentrate and Raw pellet production plants. Sampling and analysis of respirable dust, crystalline silica, and iron dust were performed according to NIOSH 0600, NIOSH 7601, and OSHA ID-121 methods, respectively. The values of lung function indices were extracted from personnel medical records over the years of their work experiences. The results showed that the highest mean concentration of respirable dust, iron, and crystalline silica dust belongs to the sweeper group and the lowest mean concentration belongs to the office group. The reduction rate of pulmonary functions over time was also higher in the sweeper and supervisor groups than in the office group. The effective factors on the pulmonary functions were age, work experience, academic education level, occupational group, cigarette and waterpipe smoking, and type of employment.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".