Fast high accuracy kinematic smartphone positioning for location-based services
Bibliographic record
Abstract
High accuracy smartphone positioning is a research topic under extensive investigation driven by the availability of raw GNSS (GPS, Galileo, GLONASS, Beidou) carrier phase observations to Android smartphones users and great value to location-based services (van Diggelen et al, 2018;Zangenehnejad & Gao, 2021).Location-based services are to find out the geographical location of the mobile user and then provide services based on the given location information and they can touch almost every aspect of our daily life from health care, transportation, emergency and rescue services, public safety, to homeland security.High accuracy spatial location information is a vital component in developing location-based service to mobile devices, including government mandates that cellular operators must provide location of emergency callers within a certain degree of accuracy.Today, nearly every new smartphone contains a GNSS chip.While the accuracy of standard GNSS positioning with smartphones usually in the range of 5 to tens of meters, improved positioning accuracy is expected to enable many new location-based applications.This is because high accuracy will make it possible to deliver personalized services of great value to mass market applications.Better positioning accuracy, for instance, can provide new experiences in lane-level navigation and augmented reality with sensor fusion.Professional apps such as surveying could also take advantage of high accuracy positioning using much cheaper multi-constellation and multi-frequency smartphones.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".