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Record W2075924251 · doi:10.1177/1420326x06072735

Evaluation of Building Materials Individually and in Combination Using Odour Threshold

2006· article· en· W2075924251 on OpenAlexafffund
Behnoush Yeganeh, Fariborz Haghighat, Lars Gunnarsen, Alireza Afshari-Safavi, Henrik Nellemose Knudsen

Bibliographic record

VenueIndoor and Built Environment · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsConcordia University
FundersNational Research Council Canada
KeywordsEnvironmental scienceAir quality indexIndoor air qualityAir pollutionBuilding materialQuality (philosophy)Computer scienceProcess engineeringArchitectural engineeringEnvironmental engineeringEngineeringCivil engineeringMeteorologyChemistry

Abstract

fetched live from OpenAlex

This paper presents results of an experimental procedure to observe the impact of building materials on perceived air quality. An untrained panel of 25 adult subjects perceived the quality of polluted air in small-scale chamber settings. The air pollution was generated by emissions from individual materials, by combinations of these materials and by mixtures of emissions from single materials. The results showed that the exposure response relationship varies for one of the tested materials compared with the others. The study also confirmed that interaction among building materials is often negligible from the perception point of view, which is in contradiction with the findings published in the literature. Further analysis of data indicated that linear addition of olfs of single materials is still a permissible simplified method to estimate the sensory pollution load in the presence of combinations of building materials in the absence of any other practical technique.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.265
Teacher spread0.227 · 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

Citations12
Published2006
Admission routes2
Has abstractyes

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