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Record W2025037771 · doi:10.3138/5512-628g-2h57-h675

Designing Interactive Sound Maps Using Scalable Vector Graphics

2006· article· en· W2025037771 on OpenAlexaffvenueabout
Glenn Brauen

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsCarleton University
Fundersnot available
KeywordsScalable Vector GraphicsJavaScriptSound (geography)Computer scienceDimension (graph theory)GraphicsSelection (genetic algorithm)World Wide WebComputer graphics (images)Artificial intelligenceAcoustics

Abstract

fetched live from OpenAlex

Geographical research and cartography have, for the most part, neglected sound, even though it is an important part of the environment within which human society exists. This article examines the use of sound in other disciplines, arguing that cartography is unusual in its relatively limited use of sound and examining research indicating that sound has the potential to provide useful options to cartographers in their map designs. The article then presents research carried out using interactive sound as an added dimension of maps designed to be accessible over the World Wide Web. The sounds are conceived as an integral part of the map, augmenting rather than replicating the visual information to provide new insights into the subject matter of the map. The article presents a detailed discussion of the design of a map of results from the Canadian federal election of 28 June 2004, showing a selection of electoral districts in the vicinity of Ottawa, ON. The map, implemented using Scalable Vector Graphics (SVG), JavaScript, and recorded audio files, uses recorded speeches by the leaders of the federal political parties to provide an auditory dimension.

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 score0.719
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.308
Teacher spread0.289 · 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

Citations29
Published2006
Admission routes3
Has abstractyes

Explore more

Same venueCartographica The International Journal for Geographic Information and GeovisualizationSame topicAnimal Vocal Communication and BehaviorFrench-language works237,207