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Record W6950989404 · doi:10.5683/sp3/hm3zbu

Attention, Working Memory and Inhibitory Control in Aging: Comparing Amateur Singers, Instrumentalists and Active Controls

2024· dataset· en· W6950989404 on OpenAlexafffund

Bibliographic record

VenueBorealis · 2024
Typedataset
Languageen
Field
Topic
Canadian institutionsConcordia UniversityUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAmateurInhibitory controlWorking memoryControl (management)Executive functionsStatistical analysis

Abstract

fetched live from OpenAlex

The aim of this study was to investigate the aging of executive functions in amateur singers and amateur instrument players. Specifically, we examined auditory processing speed, auditory selective attention, auditory and visual inhibitory control and auditory working memory in 39 amateur singers (mean age 61.8 ± 16.4; 23–88 years, 62% females), 43 amateur instrumentalists (mean age 52.1 ± 18.2; 20–88 years, 36% females) and 40 non-musician active controls (mean age 55.6 ± 19; 20–87 years, 50% females). Here we share the aggregated dataset that was used for group-level statistical analysis. In each file, all variables (independent, dependent, and covariates) that was used for analysis is included (including participants characteristics). The datasets are provided as both .XLSX and .CSV (UTF-8, separated by commas) documents for maximal compatibility. The article is available in open access here: https://nyaspubs.onlinelibrary.wiley.com/doi/10.1111/nyas.15230

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.017
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.006

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 head0.017
GPT teacher head0.258
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2024
Admission routes2
Has abstractyes

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