A user modeling approach to improving estimation accuracy in location-tracking applications
Bibliographic record
Abstract
Summary form only given. Location tracking systems are discrete in nature location information about each moving object (MO) is sampled at certain points in time. To determine the location of a MO between location reports or sometime in the future, we have to estimate the location at that point in time using the location reports we already have. The sampling frequency could affect the system's estimation accuracy as well as operating costs. Poor estimation accuracy could also carry a cost. The objective is to maximize the estimation accuracy while minimizing the operating cost. In this paper, we introduce the novel idea of using user modeling to improve the estimation accuracy of both the route and speed of a MO without the need to increase the sampling rate. We focus on a subset of moving objects we call roving users, or RUs for short. A RU is a human or human-controlled MO. The idea is that humans are creatures of habit. Knowing how a RU behaved in the past could help us estimate what he/she will do in the future. This knowledge could help us estimate the user location more accurately.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".