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Record W2005908327 · doi:10.1145/2077357.2077360

A conceptual model of trust for indoor positioning systems

2011· article· en· W2005908327 on OpenAlexafffund
Scott Bell, Ting Wei, Wook Rak Jung, Alyssa Scott

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIndoor and Outdoor Localization Technologies
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsGlobal Positioning SystemComputer scienceContext (archaeology)Positioning systemConceptual frameworkHybrid positioning systemConceptual modelInformation systemHuman–computer interactionLocation-based servicePoint (geometry)TelecommunicationsEngineeringDatabaseGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.914
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.207
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2011
Admission routes2
Has abstractyes

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