Evaluating Safe Region Sizes for Accuracy in Approximate Continuous Nearest Neighbour Queries
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Bibliographic record
Abstract
In this paper, the accuracy of the safe region approach for an approximate continuous nearest neighbour strategy is studied and evaluated. In location based services (LBS), a safe region is utilized for processing continuous queries locally on a user's device by many proposed strategies. However, for some strategies, the safe region may not contain all of the points that are in the area covered by it, which leads to approximate query processing. This paper explores a cluster-based safe region approach that is proposed in the literature. The goal is to determine if 100% accuracy can be achieved. Results show that factors such as the number and size of the clusters, the underlying cluster model, and the distribution of the data play a factor in the accuracy of the continuous query using the cluster-based safe region.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it