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Record W7077895587 · doi:10.5683/sp3/mgimlh

Subcortical Gray Matter Aging and Attentional Control in Amateur Musicians and Non-Musicians: the dataset

2025· dataset· en· W7077895587 on OpenAlexaff

Bibliographic record

VenueBorealis · 2025
Typedataset
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAmateurGray (unit)Control (management)ElectroencephalographyRange (aeronautics)Attentional control

Abstract

fetched live from OpenAlex

The objective of this study was to investigate age-related changes in subcortical grey matter structures involved in attentional control, and to examine the relationship between subcortical decline and performance on a test of attention and inhibition in adults engaged in musical and non-musical activities. Data were collected from 109 healthy adults, divided into three groups: 34 amateur singers (mean age = 61.62 ± 16.19 years; range 23–88; 65% female), 38 amateur instrumentalists (mean age = 52.47 ± 18.82 years; range 20–88; 32% female), and 37 controls (mean age = 55.57 ± 18.97 years; range 20–87; 49% female). The dataset contains aggregated measures used for group-level statistical analyses, allowing replication of the published findings and enabling new analyses on subcortical volumetric data in this sample.

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)0.007
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 score0.034
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.010

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 head0.007
GPT teacher head0.229
Teacher spread0.222 · 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
Published2025
Admission routes1
Has abstractyes

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