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Record W4283583461 · doi:10.1371/journal.pone.0270401

Assessing the ecological validity of soundscape reproduction in different laboratory settings

2022· article· en· W4283583461 on OpenAlexafffund
Cynthia Tarlao, Daniel Steele, Catherine Guastavino

Bibliographic record

VenuePLoS ONE · 2022
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsSoundscapePerceptionMetric (unit)Applied psychologyComputer scienceEcologyPsychologyEngineeringBiologyAcousticsSound (geography)

Abstract

fetched live from OpenAlex

The ever-growing body of soundscape research includes studies conducted both in everyday life environments and in laboratory settings. Yet, laboratory settings differ from in-situ and therefore may elicit different perceptions. The present study explores the ecological validity of soundscape reproduction in the laboratory using first-order Ambisonics and of different modes of questionnaire administration. Furthermore, it investigates the influence of the contextual factors of time of day, day of the week, and location on site on soundscape evaluations in situ and in the laboratory, based on the Swedish Soundscape Quality Protocol. We first tested measurement invariance between the computer-based and pen-and-paper administration of the soundscape questionnaire. We then investigated the influence of the above-mentioned contextual factors on soundscape evaluations, as well as the effect of stimuli selection in the laboratory. The analyses confirmed the underlying dimensions of proposed soundscape assessment questionnaires, confirmed metric invariance between computer and pen-and-paper, and revealed significant influences of time, day, and location on soundscape scales. This research represents a critical step in rigorously assessing soundscape evaluations in the laboratory and establishes solid evidence for the use of both in situ and laboratory soundscape studies.

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)0.002
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 score0.177
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.142
GPT teacher head0.382
Teacher spread0.240 · 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 designObservational
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

Citations28
Published2022
Admission routes2
Has abstractyes

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Same venuePLoS ONESame topicNoise Effects and ManagementFrench-language works237,207