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
The leakage of private information is of great concern on mobile devices since they contain a great deal of sensitive information. This has spurred interest in the use of taint tracking systems to track and monitor the flow of private information on a mobile device. Taint tracking systems impose memory overhead, as taint information must be maintained for every piece of information an application stores in memory. This memory cost is at odds with the growing number of low-end, memory-constrained devices, which makes up the majority mobile device growth in emerging markets. To make taint tracking affordable and to benefit a broader range of mobile devices, we present LazyTainter, which is a memory-efficient taint tracking system designed for managed runtimes. To implement LazyTainter, we enhanced TaintDroid with hybrid taint tracking, which combines lazy and eager tainting, to reduce memory usage with only negligible performance loss. Our experimental results demonstrate that LazyTainter can reduce heap usage by as much as 26.5% when compared to TaintDroid while imposing a negligible 1% increase in performance 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.
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 | 0.000 | 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