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
Despite Bluetooth's popularity, low cost, and low power requirements, Bluetooth applications remain remarkably unsophisticated. Although the research community and industry have designed games, cell-phone backup, and contextual advertising systems with Bluetooth, few such applications have been prototyped or evaluated on a large scale. Evaluating Bluetooth applications requires recruiting devices in the wild and developing robust software that can adapt to the heterogeneity of these devices. These requirements have limited both the number and the magnitude of the experiments with Bluetooth applications.This paper proposes BlueMonarch, a systemfor evaluating Bluetooth applications in the wild. BlueMonarch emulates a Bluetooth transfer to any device responding to Bluetooth Service Discovery requests; because many cell-phones, laptops, and PDAs in the wild respond to such probes, BlueMonarch enables quick prototyping of Bluetooth applications in the wild, to hundreds of unmodified Bluetooth devices. After we present the feasibility and accuracy of BlueMonarch, we use BlueMonarch to evaluate a content delivery system for Bluetooth. With BlueMonarch, we evaluated our system inside a mall and a subway system; we were able to send tens of megabytes of data to hundreds of Bluetooth devices in just a little over an hour.
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 | 0.000 |
| 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