Bibliographic record
Abstract
Pneumoconiosis, a collective term for lung diseases resulting from the inhalation of specific dusts, presents significant global public health and economic challenges1. This is particularly prevalent in traditional sectors like mining and metal industries, and emerging industries such as nuclear fuel processing. Notably, nations like China and India have over 20 million workers at risk. While awareness around pneumoconiosis has been prevalent for decades, its status as a leading occupational disease, especially in regions like China, indicates a deficiency in current mitigation policies. This essay delves into the present diagnostic and treatment measures for pneumoconiosis, its prevention strategies, worldwide trends, and the existing policies from governmental and private entities, highlighting areas for enhancement. The global resurgence of pneumoconiosis underscores the pressing need for a thorough reassessment of workplace regulations and the creation of novel standards tailored to emergent industries. Comprehensive research aimed at identifying air pollutants and associated risks in new industry workspaces is imperative. Collaborative endeavors from governmental and private sectors should focus on enhanced protective gear, workplace safety education, and comprehensive medical insurance. Innovative strategies, like forecasting potential workplace pollutants, can bridge the gap between policy enforcement and safety measures, aiming to reduce the influence of this incapacitating ailment.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".