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Record W7054581164

The association of noise sensitivity with musical aptitude and everyday use of music

2015· article· en· W7054581164 on OpenAlexaboutno aff

Bibliographic record

VenueCINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · 2015
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
FundersKansainvälisen Liikkuvuuden ja Yhteistyön Keskus
KeywordsNucleofectionHyporeflexiaTubulopathyFusible alloyDysgeusiaNoise (video)
DOInot available

Abstract

fetched live from OpenAlex

We aimed to study if noise sensitivity is associated with musical aptitude and everyday use of music. A total of 197 participants was recruited in Finland (N=91; 44 men, 47 women) and in Italy (N=106; 10 men, 96 women). The age range was from 19 to 56 years. We administered questionnaires and listening tests both online and in laboratory. Noise sensitivity was studied using the Weinstein's Noise Sensitivity Scale. Musical aptitude was tested with Seashore tests for Pitch and Time and Montreal Battery of Evaluation Amusia (MBEA). The correlation test did not show significant relationship between noise sensitivity and performance in Seashore test for Time. The correlation between noise sensitivity and the results on Pitch subscale was marginally significant indicating that subjects with lower noise sensitivity tend to perform better on pitch discrimination task. No significant correlations were found between noise sensitivity and MBEA scores. Noise sensitivity was negatively correlated with the amount of passive music listening meaning that subjects with higher noise sensitivity use music as a background more seldom than subjects with lower noise sensitivity. No association was found between noise sensitivity and the amount of active music listening a week.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.487
Threshold uncertainty score0.252

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 communication0.0000.001
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 head0.060
GPT teacher head0.233
Teacher spread0.173 · 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 designSimulation or modeling
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

Citations0
Published2015
Admission routes1
Has abstractyes

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