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Record W4415455397 · doi:10.3397/in_2025_1095787

Application of the City Ditty soundscape tool in an interdisciplinary urban park design competition

2025· article· en· W4415455397 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

VenueNOISE-CON proceedings · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsSoundscapeActive listeningAmbisonicsNatural soundsPerceptionBinaural recordingUrban designCompetition (biology)

Abstract

fetched live from OpenAlex

The user-centered soundscape design tool City Ditty has been supplemented with local data from Santiago, Chile, to be used in the design of an urban park in which the perception of natural soundscapes dominates over other sound sources. This local data comprises a 3D model of the space, ambisonic recordings of urban background noise, 3D assets and recordings of sound sources (e.g. regional birds). This sketchpad tool is being adapted to local particularities to increase its performance in biophilic design interventions in the target area, and to be able to auralize the proposed 3D park models. These interventions arise from a design competition in interdisciplinary teams and their results are evaluated through individual auralization tests. The design proposal whose soundscape gets the highest subjective rating will be optimized by iterating listening tests and adjusting the design parameters. Once optimized, the final design model can be experienced at City Ditty through head-mounted VR binaural playback as well as an immersive multi-channel playback system.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.239
Threshold uncertainty score0.462

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 studies0.0000.000
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 head0.028
GPT teacher head0.373
Teacher spread0.345 · 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