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
Current approaches for improving the reliability of web services focus on server side data collection and analysis to detect errors and prevent failures. However, significant portions of modern web applications are executed on the client browser with the server only acting as a data store. These applications are mostly developed using Javascript, which presents a challenge for developing reliable web applications due to a current lack of tools for debugging Javascript applications. In addition, these applications use AJAX to communicate with the server asynchronously; therefore they remain on the same page during their lifetime that can lead to runaway memory usage from even minor memory leaks. In this paper, we introduce MemRed, a system that improves the reliability of the client side of web applications. It achieves this goal by taking advantage of browser APIs to monitor web applications. It analyzes the collected data to detect excessive memory utilization and applies recovery action to hide failures from end users, if needed. Our prototype is implemented as an extension for the Chrome browser. The evaluation shows the effectiveness of recovery actions in lowering memory usage of web applications.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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