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Record W4288766883 · doi:10.1002/9781119887638.ch15

Control of Airborne Contaminants

2022· other· en· W4288766883 on OpenAlexaff
Professor Kerry Gardiner, Professor David Rees, Professor Anil Adisesh, Professor David Zalk, Professor Malcolm Harrington, Dr Roxane Gervais, Professor Joan Saary

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEnvironmental scienceContaminationRemote sensingGeographyBiologyEcology

Abstract

fetched live from OpenAlex

The purpose of the application of workplace control techniques is to minimise worker exposure to the potential hazard. Where chronic hazards exist, an occasional transient failure may not be too serious, but any overexposure could have grave implications in the case of sensitisers and carcinogens, and, for asphyxiants, could be fatal. The hazards can be an airborne pollutant, such as dust, gases and vapours; a microbiological organism; or a radioactive particle, all of which enter the body via the lungs. Sources of emission can be either predictable in time and space, being continuous or periodic, or unpredictable, occurring haphazardly owing to the breakdown or wear of normal engineering items. Where alternative solutions cannot be adopted, the pollutants can be captured before they are released into the ‘general working environment’ by means of extract ventilation.

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: Other · Consensus signal: Other
Teacher disagreement score0.213
Threshold uncertainty score0.787

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)0.2140.000

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 head0.014
GPT teacher head0.235
Teacher spread0.222 · 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
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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