Bibliographic record
Abstract
In Korea, asbestos related diseases (ARDs) associated with occupational and environmental asbestos exposures have been reported, and commercial products contaminated with asbestos have gathered huge public attentions recently. Review of previous studies was conducted. Whereas asbestos consumptions among developed countries have decreased, those of Asian countries have increased, which showed typical international transfer of hazardous industries. In Korea residents around former asbestos mines had ARDs, which were reported in many countries such as South Africa, Canada and Australia. ARDs among residents around asbestos factories were found in many countries such as United Kingdom, United States and Italia, and increased relative risks were reported among residents around asbestos textile factories in Korea. Increased air asbestos concentrations by environmental asbestos leakages from factories were correlated with higher malignant mesothelioma incidence rates. When air dispersion model applied, excess incidence rate as far as 2.5 km from a factory were observed. As mesothelioma incidence rate, a representative index of ARD, in Korea has not reported systemically, mandatory reporting system by health personnel who diagnose the disease needs to be introduced. It is hard to conclude that commercials with contaminated asbestos do not have adverse health effects, and further studies are needed to solve these public questions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".