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Record W4306839737 · doi:10.3390/en15207727

Reflections on the Evidentiary Basis of Indoor Air Quality Standards

2022· article· en· W4306839737 on OpenAlexaff
Christiane Berger, Ardeshir Mahdavi, Elie Azar, Karol Bandurski, Leonidas Bourikas, Timuçin Harputlugil, Runa T. Hellwig, Ricardo Forgiarini Rupp, Marcel Schweiker

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldEngineering
TopicBuilding Energy and Comfort Optimization
Canadian institutionsCarleton University
FundersPolitechnika PoznańskaVillum Fonden
KeywordsIndoor air qualityTransparency (behavior)Scope (computer science)Quality (philosophy)Context (archaeology)DocumentationRisk analysis (engineering)Architectural engineeringComputer scienceBusinessEnvironmental scienceEngineeringComputer securityGeographyEnvironmental engineering

Abstract

fetched live from OpenAlex

Buildings are expected to provide healthy and comfortable indoor environmental conditions for their users. Such conditions have diverse dimensions, including thermal, visual, air quality, auditory, and olfactory aspects. Indoor environmental quality standards, guidelines, and codes typically inform professionals in the building design and operation phase in view of procedural, contractual, and legal boundary conditions. Given this critical role of standards, it seems significant to examine the applicability and scientific validity on a regular basis. In this context, the present paper focuses on the standard-based definition of indoor air quality (IAQ) indicators and their respective values. Hence, the main aim of this effort is to study several common national and international IAQ standards in view of the scope to which they include direct or indirect evidence for the validity and applicability of their mandates and requirements. To this end, selected IAQ standards were assessed via a structured schema that includes not only basic information, quality indicators, and suggested and recommended value ranges, but also any reference to scientific studies. The findings of this effort identify certain issues with the transparency of the chain of evidence from the results of technical literature and standard-based IAQ recommendations. Moreover, recommendations are made for the development of future transparent and evidence-based IAQ standards and guidelines.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.273

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 head0.030
GPT teacher head0.307
Teacher spread0.276 · 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 designSimulation or modeling
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

Citations15
Published2022
Admission routes1
Has abstractyes

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