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
Systems continue to grow in complexity in response to the need to support vast quantities of data and a wide variety of workloads. Small changes in workloads and system configuration can result in significantly different system behaviour and performance characteristics. As a result, system administrators and developers spend many hours diagnosing and debugging performance problems in data systems and the applications that use them. In this paper, we present Sentinel, an analysis tool that assists these users by constructing fine-grained models of system behaviour and comparing these models to pinpoint differences in system behaviour for different workloads and system configurations. Importantly, Sentinel's insights are derived from built-in debug logging libraries without necessitating that their log messages be written to disk, thereby generalizing to all systems that use debug logging without incurring its overheads. Our experiments demonstrate Sentinel's superiority in analyzing the execution behaviour and performance characteristics of database systems, client applications, and web servers compared to prior approaches.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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