Ecoenergetic Comparison of HVAC Systems in Data Centers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The topic of sustainability is of high importance today. Global efforts such as the Montreal Protocol (1987) and the Kigali Amendment (2016) are examples of joint work by countries to reduce environmental impacts and improve the level of the ozone layer, the choice of refrigerants and air conditioning systems, which is essential for this purpose. But what indicators are to be used to measure something so necessary? In this article, the types of air conditioning and GWP (Global Warming Potential) levels of equipment in the project phase were discussed, the issue of TEWI (Total Equivalent Warming Impact) that measures the direct and indirect environmental impacts of refrigeration equipment and air conditioning and a new methodology for the indicator was developed, the TEWI DC (DC is the direct application for Data Center), and using the formulas of this new adapted indicator it was demonstrated that the TEWI DC for Chicago (USA) was 2,784,102,640 kg CO2/10 years and Curitiba (Brazil) is 1,252,409,640 kg CO2/10 years. This difference in value corresponds to 222.30% higher annual emissions in Chicago than in Curitiba, showing that it is much more advantageous to install a Data Center in Curitiba than in Chicago in terms of environmental impact. The TEWI indicator provides a more holistic view, helping to combine energy and emissions into the same indicator.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it