Evaluating Proximity Relations Under Uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
For location-based services it is often essential to efficiently process proximity relations among mobile objects, such as to establish whether a group of friends or family members are within a given distance of each other A severe limitation in accurately establishing such relations is the inaccuracy of dynamically obtained position data, the point in time, and the frequency with which the position data is collected. In this paper, we use the common model of interpreting the unknown position of an object by a probability distribution centered around the last know position of the object. While this approach is straight forward, it poses severe difficulties for establishing the truth or falsehood of the proximity relation. To address this problem, we analytically quantify the lower and upper bounds of the size of the smallest circle that covers the mobile objects involved in the proximity relation. Based on this result we propose two novel algorithms that closely monitor the relation at low location update cost. Furthermore, we develop a cost-effective estimation technique to determine the probability of match for a given proximity relation.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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