Bibliographic record
Abstract
In the near future Bluetooth will be embedded into many different types of mobile and portable devices. Initially this will provide simple wire replacement functions for applications such as hands-free headsets for cellular radio. However, this will also enable future picocellular services such as real-time voice and data. There are many possible applications for this such as telephone lounges in airports, shopping malls and other public places. We consider several Bluetooth-based telephony basestation (BS) designs. Since the number of SCO links per Bluetooth node is very limited, the designs consider the use of multiple overlapping Bluetooth basestations/chips. The first scheme is a direct implementation of the telephony profile where the Bluetooth basestations operate independently, without any coordination. When a mobile comes within range of the system, it associates with a basestation using the normal inquiry/page process. The second scheme, BBSM (Bluetooth basestation with migration), reduces the probability of blocking by having mobile nodes re-associate with available basestations when their current basestation is about to become blocked by SCO links. This improves the performance of the system from a blocking standpoint, but can be very spectrally wasteful. The third design, BBSS (Bluetooth basestation with standby), further improves blocking performance and decreases the wasteful effects of "node migration storms" which can occur in BBSM. We also include results for a design which uses ACL-based voice links. This scheme gives the best performance but has the disadvantage that more complex vocoding is required.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".