Crypto Currency Mining Farm for E-Vehicle using ML
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
Abstract: Globally Travelling/ Transportation charge is getting to an extreme high due to the demand of non-renewable resources like Petrol & Diesel, Electronic Toll Collection and Vehicle Parking Expenses all leads to make the travelling cost unaffordable. In order to solve this problem, the automobile industry has proposed new ideas like Electric Vehicle which will replace the usage of existing high cost non-renewable resources like Petrol & Diesel, in the same way Automobile Industry proposes a new idea to reduce the Expenses of Electronic Toll Collection Charges, Vehicle Parking Expenses, and expenses like Electric Vehicle Charging Station Bills, In this proposed system hereafter all the next generation vehicles should come up with a new technology called Crypto Currency Mining Farm(CCMF) which will do Standalone Mining in the vehicle end and earn Crypto Currencies, This Crypto Currencies will be used for meeting all types of expenses for the Vehicle , it means Vehicle will earn Crypto Currencies and spend for all the listed expenses without disturbing the Vehicle owner which will make the cost affordable.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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