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Record W4319593532 · doi:10.1080/17512549.2023.2174186

The effect of VOC and environmental parameters on ozone sensors performance

2023· article· en· W4319593532 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Building Energy Research · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsConcordia University
Fundersnot available
KeywordsOzoneRelative humidityEnvironmental sciencePollutantAir quality indexHumidityIndoor air qualityEnvironmental chemistryEnvironmental engineeringMeteorologyChemistry

Abstract

fetched live from OpenAlex

Accurate measurement of ozone concentration, especially in workplaces is a crucial component of managing indoor air quality and protecting workers’ and building occupants’ health and safety. Some factors such as gaseous pollutants (like volatile organic compounds (VOCs)), relative humidity, and air velocity and direction could interfere with monitor readings. This study examined the impact of these environmental factors on the responses of six commercial ozone monitors: three UV photometry, two electrochemical and one semiconductor metal oxide. The results demonstrated that environmental physical parameters (i.e. air velocity and relative humidity) often slightly affected UV instrument’s performance, while significant effects were seen in electrochemical and semiconductor monitors. Furthermore, chemical parameters (only VOCs including ethanol, acetone and toluene) had more influence on UV ozone monitors than those using electrochemical and metal oxide techniques.

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.

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)0.002
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 score0.517
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.026
GPT teacher head0.334
Teacher spread0.308 · 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