Cryptocurrency mining feasibility using low-cost hardware
Bibliographic record
Abstract
The development of prototypes with low-cost a nd environmentally friendly components are an important part of the characteristics of project proposals within the academic and technological area in the Faculty of Industrial Engineering of the University of Guayaquil, the present work carries out a feasibility evaluation of mining of cryptocurrency using the Raspberry Pi hardware, documenting the process and evaluating each of the parties involved, in search of a viable option that allows mining a cryptocurrency, taking into account the security of the network through the mining process, it is intended find an energetically sustainable without giving up the fundamental principles of cryptocurrencies Keywords--Cryptocurrencies, Cryptography, Raspberry Pi, Mining, Hashrate, wallet.Resumen-El desarrollo de prototipos con componentes de bajo costo y amigables al medioambiente son parte importante de las característica de propuestas de proyectos dentro del área académica y tecnológica en la Facultad de Ingeniería Industrial de la Universidad de Guayaquil, el presente trabajo realiza una evaluación de factibilidad de la minería de criptodivisas mediante el hardware Raspberry Pi, documentando el proceso y evaluando cad a uno de las partes implicadas, en busca de una opción viable q ue permita minar una criptodivisa, teniendo consideraciones a la seguridad de la red mediante el proceso de minería, se pretend e encontrar una opción energéticamente sostenible de la misma sin renunciar a los principios fundamentales de las criptodivisas.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".