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Record W2774751185 · doi:10.2495/safe-v8-n2-354-366

Determining size fractions of an aerosol in view of new definitions in polish regulation

2018· article· en· W2774751185 on OpenAlexvenueno aff
Przemysław Oberbek, Beata Kaczorowska

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAerosolEnvironmental scienceEnvironmental chemistryChemistryMeteorologyGeography

Abstract

fetched live from OpenAlex

A Polish regulation of the Minister of Labour and Social Policy on the value of the maximum concentrations of harmful chemical and dust factors in the workplace introduced new definitions of aerosols, which is "inhalable fraction and respirable fraction in place of the total dust and respirable dust". Due to the modification of the definitions of aerosol fractions, it is necessary to verify methods for determining the inhalable and respirable fraction of the aerosol. The purpose of the studies leading to the determination of the inhalable fraction and the respirable fraction of the aerosol is to obtain validated and reliable exposure information to compare the exposure to the limit value. The most reliable results of occupational exposure assessment are obtained using an individual dosimetry method. The research was carried out at seven selected workstations where there was emitted, for example, wood dust or coal dust. Dust samples were collected in a breathing zone on a specially constructed stand containing a set of sampling heads along with individual aspirators. At each of the investigated workstations, samples of the inhalable fraction and respirable fraction of the dust were collected simultaneously using different sets of sampling heads capable of sampling with varying volume air flow. These studies were conducted to compare the various samplers currently available on the Polish market. IOM was used as a reference personal sampler. By analyzing the results, it was found that the IOM sampler at each workstation obtained similar values (at least four IOM heads were used in one test). The biggest discrepancies can be seen with the GSP 3.5 sampler.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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 designSimulation or modeling
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

Citations0
Published2018
Admission routes1
Has abstractyes

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