A Navigation Assistance Agent: Mobile LBS Web Service
Bibliographic record
Abstract
<div class="htmlview paragraph">Searching the Location-Based Service (LBS) on the Internet, such as Yellow Pages, has the advantage to have the most updated information, for free (at least for the moment). Due to a number of limitations, mobile users still encounter many troubles when they use wireless device to perform online LBS searching. Providing a convenient access to the online LBS information for those wireless users is a challenging problem and also a business opportunity. In this paper, we focus on building intelligent agents to provide online LBS via wireless Internet accessing. Such an agent, based on address identification, can analyze the content of certain Web pages (ex. Yellow Pages) to search desired LBS information. We propose an ontology-based conceptual information retrieval approach combined with tree structure matching and the nearest neighbor methods to perform this task. A prototype, RouteInfo Mobile LBS for automobile drivers, has been developed to perform LBS searching, mapping and route finding.</div>
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".