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Record W1552444819 · doi:10.5668/jehs.2009.35.2.071

Health Effects of Environmental Asbestos Exposure

2009· article· en· W1552444819 on OpenAlexaboutno aff
Dongmug Kang

Bibliographic record

VenueKorean Journal of Environmental Health Sciences · 2009
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAsbestosEnvironmental healthMesotheliomaMedicineIncidence (geometry)Public healthEnvironmental protectionGeographyPathology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.289
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.278
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2009
Admission routes1
Has abstractyes

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