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Record W3025288737

No escape from the heat? Bedroom temperatures during England’s hottest summer

2020· article· en· W3025288737 on OpenAlexfundno aff
Paul Drury, Kevin J. Lomas

Bibliographic record

VenueFigshare · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilLoughborough UniversityMet OfficeMcGill University
KeywordsBedroomOverheating (electricity)Environmental healthThermal comfortGeographySocioeconomicsMeteorologyMedicineEngineeringArchaeologySociology
DOInot available

Abstract

fetched live from OpenAlex

<div>Numerous monitoring studies have demonstrated overheating of bedrooms in English homes during summer. Elevated bedroom temperatures can degrade sleep quality and impinge on health and well-being. This paper examines Public Health England’s advice to ‘move into a cooler room, especially for sleeping’ in hot conditions. Temperatures were monitored in 33 dwellings across the English Midlands between 1 May and 30 August 2018: the joint hottest English summer on record. The bedroom temperatures were analysed using the recommended CIBSE criterion that there should be no more than 1% of annual occupied hours over 26°C; adaptive comfort criteria are deemed inappropriate for sleeping persons. In half of the main bedrooms, temperatures exceeded 24°C for more than a third of sleeping hours. The CIBSE overheating criterion was</div><div>exceeded in 78% of master bedrooms. Even if everybody in a household slept in the coolest bedroom, 70% would still experience overheating. Assessing the living room as a bedroom led to a substantial reduction in homes classed as overheating. It is concluded that, whilst public health advice to seek cooler spaces during hot weather is well founded, such ‘safe havens’ for sleeping may exist only for a minority of English households. Further work is, however, needed.</div><div><br></div>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.974

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)0.5010.027

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 head0.074
GPT teacher head0.273
Teacher spread0.199 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2020
Admission routes1
Has abstractyes

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