Bibliographic record
Abstract
People are getting familiar with cryptocurrencies because of the rapid development of cryptography, and bitcoin, a traditional decentralized digital currency, becomes famous. Thus, it is necessary to establish a digital currency allocation framework. Two existing methods both share the same goal of reaching blockchain consensus; however, the processes are different: The proof of Work system is completely related to tasks, but the Proof of Stake system is related to tokens. Hence, service providers are more than glad to apply the Proof of Work theory after distinguishing the difference between these two systems; this system which does not have high limitations is more fair and balanced. To enhance the traditional Proof of Work system, Artificial Intelligence can properly help and make the new framework works more efficiently. AI model can pre-assign a trustworthy score via the IP address, and then it can take the responsibility to generate the puzzle for the qualification. After the model verifies the output, the trustworthy score can increase or decrease based on the performance. Finally, it can establish a loop from the trustworthy score to puzzle difficulty, and then back to the trustworthy score. Therefore, an AI assistant can accurately monitor the entire transaction process and ensure validation to be environmentally friendly.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".