Bibliographic record
Abstract
In the last decade, personal computers became more powerful and less expensive, and the development of Internet increased the connectivity of these machines. This situation opened many opportunities in the distributed computing world. Some projects such as SETI@home were launched to perform scientific computations on temporarily unused workstations. However, in most cases, the distributed computing software installed on the workstations could only execute predetermined applications. It was necessary to update the software to enable it to execute new applications. In this paper we present Snowflakes, a distributed computing software designed to download and execute arbitrary applications automatically. Like SETI@home, Snowflakes provides a screen saver that can be installed on the workstations of a laboratory to harness their computing power without disrupting the work of the users. We considered carefully the issues of security and ease of use. The applications distributed are authenticated and sandboxed to prevent accidental or malicious damage to the user machines. The software itself is easy to install and easy to configure with its graphical user interfaces. Moreover, Snowflakes features a simple API that enables a programmer to develop new applications rapidly.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".