RETRACTED: A review on the energy in buildings: Current research focus and future development direction
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Currently, although energy conservation related research in buildings is a matter of great urgency in the context of an ever more serious energy crisis, people seem to pay more attention on the field of civil engineering, such as the design, construction, monitoring and maintenance management of building structures. This is also evidenced by the authors' extensive research and strong practical engineering experience in infrastructure projects such as bridges. This study first presents the general building energy situation. The state of the art of the energy in buildings is then reviewed, followed by pointing out the intelligent monitoring-based future direction, and then the final goal towards the smart city can be expected. Specifically, more than one hundred published papers are selected for sample analysis, taking into account different research topics and different publication dates etc. The research topics, research methods and research conclusions of these published papers are very different, and they have not yet produced results that could be generally accepted. Actually, most of the published papers focus on the analysis and conservation of building energy, including the energy model for analysis and prediction, the energy affected by resident behavior and building forms, the renewable energy utilization and zero energy building. While a small part of the published papers is concerned with the resilient structural energy dissipation and collapse-resistant. Furthermore, the intelligent monitoring of building energy, supported by advanced sensor development and big data analysis technology, is also providing us a more promising future on the way to the smart city. It should be further noted that the design and construction codes or standards related to building energy have not yet been retrieved, and these have a strong guiding significance for engineering practice. Therefore, more research needs to be done to expect a better practical outcome.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".