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
As the pandemic, Covid-19, spreading across the world from 2020, it changes the habits of people. It helped the development of the online movement. Cryptocurrency investment was one of them. Ethereum is one of the most significant blockchain-based platforms and the second largest proportion of the cryptocurrency market. The price of Ethereum was examined from the last 3 years. The result shows that the price of Ethereum increases drastically at the beginning of the pandemic due to different influences of Covid-19. However, it is decreasing as Covid-19 has become a normal illness to handle recently. In summary, Ethereum is in a strong correlation with Covid-19 and still can fluctuate by illness or movement that increases the interaction of people on the internet. In this paper, vector autoregression model and ARMA-GARCHX model was constructed where VAR model helped to find the relationship between the new infections of COVID-19 in China and Overseas and the return rate of Ethereum and ARMA-GARCHX model was applied to analyze the volatility of the return and predict the future return rate. The models suggest that the return rate can be affected if the number of new infections increases in a short period. However, the number of new infections is not significant to the volatility of the return rate of Ethereum.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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