Synthetic Evaluation of Multi-Criteria Decision-Making Algorithms in Energy-Efficient Buildings
Bibliographic record
Abstract
Multi-criteria decision making (MCDM) on energy-efficient buildings has become essential in both the industry and academia as construction projects grow increasingly complex. With a prime goal of increasing its effectiveness, MCDM research has witnessed tremendous growth over the past three decades. Despite the necessity to monitor the research growth of a research topic to identify its trends and gaps, and hence shed light on research areas that warrant future research attention, there is a lack of systematic literature analysis in MCDM area. To fill this gap, this paper recruited a mixed-review method of scientometric and systematic reviews of 56 research papers on seven selected popular MCDM techniques published from 2010 to March 2021. The scientometric review identified the most prolific journals, keyword correlations, and geospatial connections between research countries in the MCDM area. On the other hand, the systematic review analysis showed that there are five main research topics in MCDM. Furthermore, the major approaches applied in MCDM research were investigated.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".