GPS Localization Accuracy Classification: A Context-Based Approach
Bibliographic record
Abstract
Global Positioning System (GPS) localization has been attracting attention recently in various areas, including intelligent transportation systems (ITSs), navigation systems, road tolling, smart parking, and collision avoidance. Although, various approaches for improving localization accuracy have been reported in the literature, there is still a need for more efficient and more effective measures that can ascribe some level of accuracy to the localization process. These measures will enable localization systems to manage the localization process and resources to achieve the highest accuracy possible and to mitigate the impact of inadequate accuracy on the target application. The localization accuracy of any GPS system depends heavily on both the technique it uses to compute locations and the measurement conditions in its surroundings. However, while localization techniques have recently started to demonstrate significant improvement in localization performance, they continue to be severely impacted by the measurement conditions in their environment. Indeed, the impact of the measurement conditions on the localization accuracy in itself is an ill-conditioned problem due to the incongruent nature of the measurement process. This paper proposes a scheme to address localization accuracy estimation. This scheme involves two steps, namely, measurement condition disambiguation and enhanced location accuracy classification. Real-life comparative experiments are presented to demonstrate the efficacy of the proposed scheme in classifying GPS localization accuracy under various measurement conditions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".