A conceptual model of trust for indoor positioning systems
Bibliographic record
Abstract
Ubiquitous positioning requires services that are supplemental to the existing Global Positioning System (GPS). For spaces where GPS does not work (indoors, canyons, etc.) augmented or enhanced positioning systems are necessary. For such systems to function appropriately users must have a GPS-like experience. In essence, users of supplementary positioning systems must trust the information these systems deliver. In order to develop systems that mimic the trust generated by GPS and to better understand the implications of features or changes to such a positioning system we believe a conceptual model of positioning system trust is necessary. Such a conceptual model must consider several aspects of the user and the system. The system must be accurate, with an informative User Interface that is transparent (provides context and background on positions and how they are calculated), it must use verified source data, and provide information that supports a range of users. In this paper we present the essential elements of trust for enhanced or supplementary positioning systems.
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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) | 0.000 | 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".