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Record W4400075399 · doi:10.1093/annweh/wxae035.253

263 Emission factors and fume generation rates of particles from various welding processes

2024· article· en· W4400075399 on OpenAlexaff
Mozhgan Khaligh, Zaher Hashisho, Maximilien Debia, Doug Hamre, Gentry Wood, Bernadette Quémerais

Bibliographic record

VenueAnnals of Work Exposures and Health · 2024
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsUniversité de MontréalUniversity of Alberta
Fundersnot available
KeywordsWeldingMaterials scienceEnvironmental healthMetallurgyMedicine

Abstract

fetched live from OpenAlex

Abstract Welding fumes are classified as a class 1 carcinogen, according to IARC. A conical chamber was built to determine emission factors and fume generation rates of particles from various welding processes. The chamber was calibrated according to the American Welding Society (AWS) standard F1.2:2013 and a high-volume air sampling pump was fixed to the top of the chamber to collect the fumes on 293 mm glass fibre filters located between the cone and the pump. Two welding processes were tested: gas metal arc welding (GMAW) and low power density laser beam welding (LLBW). Tests were performed using seven different types of consumables and different base materials. Consumables included ER70S-2, ER70S-6, ER70S-3, ER308L, ER316L, E316LSi, and ER5356 and the base materials included mild steel, stainless steel, and aluminium. Except for aluminium, which could not ne weld with this particular LLBW machine, the same consumables were used for GMAW and LLBW. Three levels of welding voltages in GMAW and power settings in LLBW were used for each type of consumable to see the effect of voltage and power on particle emission factors and fume generation rates. Additionally, differences between particle emission factors and fume generation rates from GMAW and LLBW for each consumable were compared. Emission factors and fume generation rates increased with increasing voltage/power. Although the fume generation rates were lower for LLBW than for GMAW, the emission factors were higher for LLBW.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.247

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 head0.113
GPT teacher head0.347
Teacher spread0.234 · 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
Published2024
Admission routes1
Has abstractyes

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