RETRACTED: An innovative simulation-based methodology for evaluating cooling strategies in climate change-induced overheating
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
As global climate change progresses the built environment grapples with the increasing challenge of overheating. In response to these challenges, this study introduces an innovative simulation-based methodology aimed at enhancing the resilience and sustainability of cooling strategies. The proposed methodology utilizes weather and building data characterization, user clothing behavior, and cooling strategy selection. The methodology relies on three main indicators: Indoor Overheating Degree (IOD), Ambient Warmness Degree (AWD), and Climate Change Overheating Resistivity (CCOR). It also considers sub-indexes like analysis of greenhouse gas emissions, energy consumption, Exceedance Hours, and the average Predicted Percentage of Dissatisfaction. This comprehensive approach allows for a multi-zonal assessment of indoor overheating risk and resilience to climate change, to validate the methodology ‘s effectiveness, we conducted a thorough comparison, focusing on Packaged Terminal Heat Pumps (PTHP) (C01) and Fan Coil Units (FCU) (C02) in six selected reference cities with different climates. The case study employs a shoebox model to depict a double-zone office and administration building. In general, C01 demonstrates greater resistance to climate change-induced overheating compared to C02. The highest CCOR value, 58.16, is found in C01 in Vienna, while the lowest CCOR value, 12.7, is observed in C02 in Montreal, indicating the lowest resistance. Furthermore, this study underscores the pivotal importance of meticulously evaluating the susceptibility of cold cities to the imminent impacts of climate change and the urgency of implementing proactive strategies to bolster their resilience.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".