Bibliographic record
Abstract
For thousands of years, location information has been used for navigation. This has changed in the last century as advances in wireless communication and microelectronics have given birth to mobile computing devices . These devices enable their users to access sensing and computing capabilities from anywhere and at any time. An important consequence of such mobility is that the resource and information needs of wireless users are no longer fixed but vary with their changing location and, more generally, with their changing context . This has sparked a new generation of applications that employ location information to cater to the changing needs of mobile users. These applications, known as location-based services (LBS), are offered on top of wireless communication infrastructures to add value to existing services. To enable and support the delivery of LBS, accurate, reliable, and realtime user location information is needed. This need has incited a new interest in positioning and tracking systems whose aim is to determine the physical coordinates of a wireless mobile device carried by a human user. The focus of this book is one class of positioning systems that employ radio signals from wireless local area networks (WLAN) for positioning. These systems are of special interest as they are able to provide high positioning accuracies in indoor and outdoor environments with minimal deployment and maintenance costs. This book is divided into two parts. The first part focuses on topics related to the history and applications of positioning systems.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.399 | 0.256 |
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".