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The Effect of CO Emissions on Lung Vital Capacity in Vehicle Inspectors at the Transportation Department in Slawi

2025· article· en· W4407723012 on OpenAlexaff
Yanri Wijayanti Subronto, Siti Maimunah, Emilya Nurjani

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsBusinessEnvironmental scienceWaste managementTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Carbon monoxide is a colorless and odorless gas that can cause various diseases, one of which is respiratory disease. The quantitative research method used a cross sectional approach and was analytic observational. Measurement of carbon monoxide at PKB Slawi building with an interval of 15 minutes obtained an average CO gas of 89.36 ppm. The analysis used was univariate and bivariate. Test used by Fisher’s Exact Test. Of the 20 workers, there were 12 workers who had lung function disorders,and 8 workers have normal lung. Tests obtained a p value of 0.005 <0.05, which means that it has a relationship between exposure to CO emissions and impaired lung function. Meanwhile, for the confounding variable, each variable was obtained for a P value> 0.05 with a significance level of 95% so that Ho was accepted, Ha was rejected which means that theres no relation between the various variable with lung function.

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 score0.061
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.017
GPT teacher head0.259
Teacher spread0.242 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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