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
Linux is increasingly used to power everything from embedded devices to supercomputers. Developers of such systems often start with a mainline kernel from kernel.org and then apply patches for their application domain. Many of these patches represent crosscutting concerns in that they do not fit within a single program module and are scattered throughout the kernel sources--easily affecting over a hundred files. It requires nontrivial effort to maintain such a crosscutting patch, even across minor kernel upgrades due to the variability of the kernel proper. Moreover, it is a significant challenge to ensure the kernel's correctness when integrating multiple crosscutting concerns. To make matters worse, developers use simple code merging tools that directly manipulate source file lines instead of relying on a lexical, grammatical, or semantic level of abstraction. The result is that patch maintenance is extremely time consuming and error prone. In this paper, we propose a new tool, called c4, designed to help manipulate patches at the level of their abstract syntax and semantics. We believe our approach will simplify the management of OS variations and thereby improve OS evolution.
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 | 0.001 | 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 | 0.000 | 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 it