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Record W2941767078 · doi:10.1080/15459624.2019.1603388

Exposures to air contaminants in compartment fire behavior training (CFBT) using particleboard fuel

2019· article· en· W2941767078 on OpenAlexfundno aff
Katherine M. Kirk, Michael Logan

Bibliographic record

VenueJournal of Occupational and Environmental Hygiene · 2019
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
FundersCalgary Firefighters Burn Treatment SocietyQueensland Health
KeywordsFirefightingEnvironmental scienceContaminationCombustionCombustion productsInhalation exposureEnvironmental chemistryWaste managementVentilation (architecture)InhalationMeteorologyChemistryEngineeringEcologyMedicine

Abstract

fetched live from OpenAlex

Firefighters are exposed to a variety of combustion products during operational fires but also during live-fire training. As part of an on-going project investigating firefighter operational and training environments, this study measured the atmospheric concentrations of volatile organic compounds and acid gases outside and inside the structural firefighting ensembles worn by instructors during compartment fire behavior training using particleboard as a fuel. Atmospheric concentrations of benzene, formaldehyde, and hydrogen cyanide within the firefighting environment were observed to exceed Australian workplace exposure standards; although, the use of self-contained breathing apparatus throughout the training meant that atmospheric concentrations measured were not representative of firefighter inhalation exposures. Concentrations of air contaminants inside the structural firefighting ensembles during compartment fire behavior training were substantially lower than outside the ensembles, and much lower than those documented as potentially causing acute toxic effects in humans by dermal absorption from vapor. Although this study is focused on the generation of air contaminants in compartment fires, dermal absorption in these types of training environments may still constitute a potential route of low-level exposure to some combustion products.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.418

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 head0.414
Teacher spread0.301 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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