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
Recovery from intrusions is typically a very time-consuming operation in current systems. At a time when the cost of human resources dominates the cost of computing resources, we argue that next generation systems should be built with automated intrusion recovery as a primary goal. In this paper, we describe the design of Taser, a system that helps in selectively recovering legitimate file-system data after an attack or local damage occurs. Taser reverts tainted, i.e. attack-dependent, file-system operations but preserves legitimate operations. This process is difficult for two reasons. First, the set of tainted operations is not known precisely. Second, the recovery process can cause conflicts when legitimate operations depend on tainted operations. Taser provides several analysis policies that aid in determining the set of tainted operations. To handle conflicts, Taser uses automated resolution policies that isolate the tainted operations. Our evaluation shows that Taser is effective in recovering from a wide range of intrusions as well as damage caused by system management errors.
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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it