Thermal Conditions in Indoor Environments: Exploring the Reasoning behind Standard-Based Recommendations
Bibliographic record
Abstract
Professionals in the building design and operation fields typically look at standards and guidelines as a reliable source of information and guidance with regard to procedural, contractual, and legal scope and requirements that are relevant to accountability issues and compliance necessities. Specifically, indoor environmental quality (IEQ) standards support professionals to bring about comfortable thermal, air quality, acoustic, or visual conditions in buildings. In this context, it appears essential to regularly examine the IEQ standards’ applicability and scientific validity. The present contribution focuses on common thermal comfort standards in view of the reasoning and includes evidence behind their recommendations and requirements. Thereby, several international and national thermal comfort standards are examined via a structured matrix to assess basic parameters, design and performance variables targeted by the standards, suggested value ranges, and both general and specific evidence from the scientific literature. Finally, this paper discusses findings and points to the identified gaps in the chain of evidence from the results of scientific studies and the recommendations included in the thermal standards. As such, the present contribution has the potential to inform future developments regarding transparent and evidence-based thermal standards.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.125 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".