Energy Consumption, Pandemic Period and Online Academic Education: Case Studies in Romanian Universities
Bibliographic record
Abstract
Objectively, electricity is the most important current power source and, especially, of the future. Electricity consumption in universities worldwide is achieved by installations and equipment both in laboratories, classrooms, applications, and sports, in the rooms of administrative staff, and campus. Depending on the specializations, each university has a specific footprint of electricity consumption. During the pandemic of 2020, the world introduced several measures to limit the spread of the Covid virus, including online education in academia. Thus, in terms of electricity consumption, universities recorded a decrease during the pandemic, but there was an increase in household consumption for teachers and students. The subject of this paper is a quantitative analysis of the data provided by four universities in Romania on electricity consumption recorded before and during the pandemic. These data are correlated with household electricity consumption for the same two time periods, which were collected from some teachers and students in a university. A percentage of the decrease respectively increase of these electricity consumptions is due to the use of computers in the university respectively at home. That is why the measurements performed on a personal computer connected to the Teams platform used in the online education system are presented and analyzed. All these data and comparative analyses are especially useful for any university in the country or worldwide.
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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 | 0.000 | 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 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".