Recon
Bibliographic record
Abstract
File system bugs that corrupt metadata on disk are insidious. Existing reliability methods, such as checksums, redundancy, or transactional updates, merely ensure that the corruption is reliably preserved. Typical workarounds, based on using backups or repairing the file system, are painfully slow. Worse, the recovery may result in further corruption. We present Recon, a system that protects file system metadata from buggy file system operations. Our approach leverages file systems that provide crash consistency using transactional updates. We define declarative statements called consistency invariants for a file system. These invariants must be satisfied by each transaction being committed to disk to preserve file system integrity. Recon checks these invariants at commit, thereby minimizing the damage caused by buggy file systems. The major challenges to this approach are specifying invariants and interpreting file system behavior correctly without relying on the file system code. Recon provides a framework for file-system specific metadata interpretation and invariant checking. We show the feasibility of interpreting metadata and writing consistency invariants for the Linux ext3 file system using this framework. Recon can detect random as well as targeted file-system corruption at runtime as effectively as the offline e2fsck file-system checker, with low overhead.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.045 |
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".