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Record W1996693523 · doi:10.3138/carto.48.3.1641

Vertical Colour Maps – A Data-Independent Alternative to Floor-Plan Maps

2013· article· en· W1996693523 on OpenAlexvenueno aff
Alexander Salveson Nossum, Hengshan Li, Nicholas A. Giudice

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2013
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsnot available
Fundersnot available
KeywordsFloor planPlan (archaeology)Digital mappingComputer scienceDimension (graph theory)Cognitive mapOrientation (vector space)Space (punctuation)CartographyGeographyCognitionEngineering drawingEngineeringMathematics

Abstract

fetched live from OpenAlex

Location sharing in indoor environments is limited by the sparse availability of indoor positioning and lack of geographical building data. Recently, several solutions have begun to implement digital maps for use in indoor space. The map design is often a variant of floor-plan maps. Whereas massive databases and GIS exist for outdoor use, the majority of indoor environments are not yet available in a consistent digital format. This dearth of indoor maps is problematic, as navigating multistorey buildings is known to create greater difficulty in maintaining spatial orientation and developing accurate cognitive maps. The development of standardized, more intuitive indoor maps can address this vexing problem. The authors therefore present an alternative solution to current indoor map design that explores the possibility of using colour to represent the vertical dimension on the map. Importantly, this solution is independent of existing geographical building data. The new design is hypothesized to do a better job than existing solutions of facilitating the integration of indoor spaces. Findings from a human experiment with 251 participants demonstrate that the vertical colour map is a valid alternative to the regular floor-plan map.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.802

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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 designNot applicable
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

Citations2
Published2013
Admission routes1
Has abstractyes

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