Comparative Analysis of Energy Consumption of Cryptocurrencies with Experimental Study Approach in the Scope of Green Computing
Bibliographic record
Abstract
Many technologies enter our lives with the great advancement in information technology. These technological developments affect and change our life directly. According to affection of our life, cryptocurrency is the most popular technology. This technology, which is a relatively mixture of currency and cryptology, is used all over the world with increasing acceleration. So, cryptocurrency technology is still used to make payments without banks and is considered as virtual currency. Besides of this opportunity, cryptocurrency has few challenges. Most dangerous and critical challenge for environment is energy consumption in mining of cryptocurrency. Blockchain is the technology which is used in mining process and it consumes more and more energy. According to this critical challenge, Ethereum which is one of the popular cryptocurrencies, was used in an experimental study to analyze energy consumption. Experiment examined the data from Mar 30, 2017 to Dec 30, 2019. These data were compared with another popular cryptocurrency which is bitcoin in order to find the better one for environment. In this study, the data from 196 GPUs were examined and electricity consumption and gain were analyzed. Totally, 3 different types of GPU brands were used, and the brand of the units, the power consumed, and the electricity unit price in the country where it was tested were analyzed respectively.
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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 | 0.001 | 0.003 |
| 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".