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Record W2898183293 · doi:10.5539/gjhs.v10n11p188

Safe Concentration of Benzene Exposure in Work Environment at Motor Workshop

2018· article· en· W2898183293 on OpenAlexvenueno aff
Abdul Rohim Tualeka, Frans Salesman, Juliana Jalaluddin, Atjo Wahyu

Bibliographic record

VenueGlobal Journal of Health Science · 2018
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsnot available
FundersUniversitas Airlangga
KeywordsBenzeneChristian ministryFlame ionization detectorObservational studyEnvironmental chemistryPopulationMaximum Allowable ConcentrationGas chromatographyChemistryToxicologyEnvironmental scienceEnvironmental healthMedicineChromatographyOrganic chemistryBiologyInternal medicinePolitical science

Abstract

fetched live from OpenAlex

Benzene is a colorless liquid that can evaporate rapidly in air and slightly dissolved in water. Exposure of benzene to the body has a very adverse impact on health. The aims of this research were to know benzene risk characteristic or RQ, and safe concentration of benzene exposure in a workshop environment. This research was observational, cross-sectional design with a population of 7 workers of the motor industry in Surabaya. The benzene exposure in the workplace was measured by Gas Chromatography-Flame Ionization Detector (GC-FID). Data analysis was done by using quantitative data. Maximum benzene intake received by workers was 0.1837 mg/kg/day. RQ on average workers more than 1 (> 1), with the highest RQ of 22.673. The highest safe concentration of workers was 3.9 mg/m3 and the lowest safe concentration was 0.4 mg/m3. The concentration of benzene exposure in the motor industry showed was above the threshold limit. According to the regulation of Manpower and Transmigration Ministry No 13 the year 2011, RQ for benzene showed a high-risk impact for workers, the smallest safe concentration for the worker was 0.4 mg/m3.

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)0.002
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 score0.058
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.026
GPT teacher head0.334
Teacher spread0.308 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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