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Location‐Based Services

2017· other· en· W1512714298 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Encyclopedia of Geography · 2017
Typeother
Languageen
FieldSocial Sciences
TopicHuman Mobility and Location-Based Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeospatial analysisLocation-based serviceVariety (cybernetics)Context (archaeology)Computer scienceGeolocationData scienceSpatial contextual awarenessWorld Wide WebGeographyTelecommunicationsCartography

Abstract

fetched live from OpenAlex

The past decade has seen major advances in location‐based technologies and geospatial information. Gradually, the public's awareness and consideration of the spatial aspects of life are increasing, due to the ubiquity of so‐called location‐based services (LBS). While popular navigation or mapping services like Google Maps have contributed to this “revolution,” today's LBS include an amazing variety of services and are imposing themselves in a much larger spectrum of daily activities. The current evolution of LBS suggests that in the near future, when certain technological challenges have been overcome, geolocated information about a large number of people and objects in our environment will be available anytime and anywhere, in a way that will be tailored to users' needs, and LBS will be embedded in daily life. Within this context, this entry reviews the current state of LBS‐related issues and research, starting with an overview of historical development and current applications, an analysis of the impacts of LBS, and a discussion of current and future challenges that the research community is facing in shaping the future of LBS.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.445
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.008
GPT teacher head0.291
Teacher spread0.283 · 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