A solution to the ill-conditioned GPS accuracy classification problem: Context based classifier
Bibliographic record
Abstract
GPS localization has been attracting significant attention recently in many areas. Intelligent transportation systems, navigation systems, road tolling, and collision avoidance systems, are examples of applications that utilize the GPS technology for localization. However, localization accuracy remains a key issue that prevents such applications from delivering on their ultimate promise. The localization accuracy of any GPS system depends heavily on the methodology it uses to compute locations as well as the measurement conditions in its surrounding. The impact of the measurement conditions on the localization accuracy in itself is an intricate ill-conditioned problem due to the incongruent nature of the measurement process. This paper proposes a novel scheme to address localization accuracy. The scheme involves three steps, namely, classify instantaneous GPS accuracy based on measurement conditions, consolidate the consecutive classifications of the GPS Accuracy, and enhance the robustness of classifying the GPS accuracy for the following measurements. Real life comparative experiments are conducted to demonstrate the efficacy of the proposed scheme in classifying the GPS accuracy under various measurement conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".