Folding at home: Artificial intelligence and crypto symbiosis for the science
Bibliographic record
Abstract
Abstract Folding at Home community gathers pecuniary crypto interest to an altruist cause (medical drugs research participation). There are many possibilities of ‘teams’ for folding, some of which allow compensation in cryptomonies. The authors will therefore study the three major communities in order to compare them, in a cost‐benefit study logic. A cost‐benefit analysis was performed between CURECOIN, BANANO, DOGECOIN folding, DOGECOIN mining and their communities on social platforms based on several outcomes: ‘Points Per Day (PPD)’, ‘Whattomine Mh/s Equivalent’, ‘Graphics Processing Units (GPU) Thermal Design Power/Typical Board Power Watts’, ‘Coins per 1,000,000 PPD’, ‘Coins per Day’, ‘Cost Per Coin’, ‘Cost Per Day’, ‘kWh Used Per Day’. Actually, BANANO, thanks to a large community and bots, has the highest PPD production and the lowest energetic cost on Central Processing Unit per week. On GPUs, DOGECOIN folding has the lowest weekly cost. However, the DOGECOIN community cannot produce as many PPD as the Banano team. CURECOIN offers a good compromise between the environmental point of view and the profitability one. To ensure fairness, the choice of the crypto and the way to earn it that has to be solved should be a pure public good, that is, perfectly non‐rival in consumption and non‐excludable. Indeed, the primary goal of those three communities is to allow scientific progress (thanks to Folding at Home) before allowing a return on investment.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".