Bibliographic record
Abstract
In typical file systems, valuable data is vulnerable to being accidentally or maliciously deleted or overwritten. Versioning file systems protect data from accidents by transparently retaining old versions, but do less well in protecting data from malicious attack. These systems remain vulnerable to attackers who gain unauthorized access to prune old file versions, who bypass the file system to directly manipulate storage, or who exploit bugs in any part of the operating system. This paper presents VDisk, a secure, block-level versioning system that adds file-grain versioning to a standard, unmodified file system. VDisk consists of a set of untrusted user-mode tools and a trusted, secure kernel that is implemented within an isolated Xen virtual machine domain. The secure kernel is designed to be simple and thus trustworthy. This kernel logs file-system updates to a secure log, exports a read-only view of the log to the rest of the system and securely removes unwanted versions from the log. Secure cleaning is implemented in a two-level manner. An untrusted, user-mode cleaner selects log entries for reclamation and submits cleaning requests to the trusted VDisk kernel along with a proof that the request satisifies the device's version-retention policy. The secure kernel verifies the proof and updates the log.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".