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Record W4400354557 · doi:10.1093/occmed/kqae023.0749

P-207 WHY YOU SHOULD KNOW HOW YOUR AEROSOL SAMPLER IS PERFORMING

2024· article· en· W4400354557 on OpenAlexaboutno aff
Steven Verpaele

Bibliographic record

VenueOccupational Medicine · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsAerosolNeed to knowEnvironmental scienceMedicineComputer scienceMeteorologyPhysicsComputer security

Abstract

fetched live from OpenAlex

Abstract Introduction and methods A recent survey of aerosol sampling heads used within the metals industry was done in parallel to a survey of European laboratories concerning the methods used for the determination of nickel in workplace air. Results This survey revealed a wide variety of inhalable, thoracic, and respirable samplers as being commonly used for measuring metals and metalloids exposure in workplaces. Discussion In April 2019, the Nickel Institute, a global association of primary nickel producers, held a meeting with interested parties regarding the development or adaptation of existing sampling trains to measure low levels of metals and metalloids in the workplace. The parties involved agreed on the need for international sampler comparison studies. The main objective of these studies is to compare currently used (and validate any newly developed) personal samplers for measuring particulate-related exposure (and more specifically metals and metalloids) in workplace settings. Sampler efficiency studies for relevant aerosol size fractions of those samplers currently on the market will also be included in this effort. Conclusion In this presentation, an overview of why sampler comparison and efficiency studies are needed will be provided, together with recent outcomes of a laboratory and field study funded by Worksafe BC (Canada).

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score0.997

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)0.0040.000

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 head0.057
GPT teacher head0.315
Teacher spread0.258 · 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.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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