MétaCan
Menu
Back to cohort
Record W2985893746 · doi:10.3390/su11226225

TVOCs and PM 2.5 in Naturally Ventilated Homes: Three Case Studies in a Mild Climate

2019· article· en· W2985893746 on OpenAlexfundno aff
Jésica Fernández-Agüera, Samuel Domínguez-Amarillo, Marco Fornaciari, Fabio Orlandi

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
FundersMcGill University
KeywordsEnvironmental scienceVentilation (architecture)Indoor air qualityStock (firearms)Environmental engineeringAtmospheric sciencesMeteorologyGeographyArchaeology

Abstract

fetched live from OpenAlex

In southern Europe, the present stock of social housing is ventilated naturally, with practice varying in the different seasons of the year. In winter, windows are kept closed most of the day with the exception of short periods for ventilation, whereas the rest of the year the windows are almost permanently open. In cold weather, air changes depend primarily on the air infiltrating across the envelope and when the temperature is warm, on the air flowing in through open windows. CO2, PM2.5, and TVOC concentration patterns were gathered over a year’s time in three social housing developments in southern Europe with different airtightness conditions and analyzed to determine possible relationships between environmental parameters and occupants’ use profiles. Correlations were found between TVOC and CO2 concentrations, for human activity was identified as the primary source of indoor contaminants: peak TVOC concentrations were related to specific household activities such as cooking or leisure. Indoor and outdoor PM2.5 concentrations were likewise observed to be correlated, although not linearly due to the presence of indoor sources. Ventilation as presently practiced in winter appears to be insufficient to dilute indoor contaminants in all three buildings, nor does summertime behavior guarantee air quality.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.034
GPT teacher head0.359
Teacher spread0.325 · 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 designObservational
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

Citations34
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicAir Quality and Health ImpactsFrench-language works237,207