Bibliographic record
Abstract
The next generation mobile networks and their services are being designed to easily accommodate the growth and changes in technology that will occur during their operational lifetime. One such service is the location service. This may be used to provide location information for subscriber services (e.g. summon a taxi to the subscriber's current location), for "emergency services" (e.g. summon medical assistance to the subscriber's location) and to assist the mobile network's internal operations (e.g. location dependent handover). Technology is rapidly developing in this area, both within the mobile networks and outside (e.g. satellite GPS). A client server architecture provides an efficient and flexible design that can accommodate both the growth in service requirements and the changes in technology that will occur within the mobile networks. As the service and the technology develop, the measurement process and the servers may be upgraded to introduce new capabilities. In this way the network operator is assured of maintaining the most efficient and up-to-date capabilities and technology for the services within their network. This paper reviews the client server architecture and operations developed within the 3GPP UTRAN "third generation" mobile standards and their applications to future communications networks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".