MétaCan
Menu
Back to cohort
Record W2158256258 · doi:10.1109/imtc.2011.5944050

Trace amount formaldehyde gas detection for indoor air quality monitoring

2011· article· en· W2158256258 on OpenAlexaffabout
Gaozhi Xiao, Zhiyi Zhang, John Weber, Heping Ding, H. McIntosh, D. Desrosiers, Gang Nong, Doyun Won, Jeffrey Dunford, James J. Tunney, K. Darcovich, Gerardo A. Diaz‐Quijada

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsFormaldehydeIndoor airIndoor air qualityEnvironmental scienceTrace gasTRACE (psycholinguistics)Environmental chemistrySick building syndromeWaste managementChemistryEnvironmental engineeringOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Formaldehyde is not only a carcinogenic chemical, but also causes sick building syndrome. Very small amounts of formaldehyde, such as those emitted from building materials and furniture, pose great concerns for human health. A Health Canada guideline, proposed in 2005, set the maximum formaldehyde concentration for long term exposure (8-hours averaged) as 40 ppb (50 μg/m3). This is a low concentration that commercially available formaldehyde sensors have great difficulty to detect both accurately and continuously. In this paper, we report a formaldehyde gas detection system which is capable of pre-concentrating formaldehyde gas using absorbent, and subsequently thermally desorbing the concentrated gas for detection by the electrochemical sensor. Initial results show that the system is able to detect formaldehyde gas at the ppb level, thus making it feasible to detect trace amount of formaldehyde in indoor environments.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.042
GPT teacher head0.265
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations16
Published2011
Admission routes2
Has abstractyes

Explore more

Same topicAdvanced Chemical Sensor TechnologiesFrench-language works237,207