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Regions with altered probabilistic grey volume in the somatosensory-motor cortex of handball players derived from the voxel-based morphometric group analysis (A-C) and the correlation analysis (D and E).

2015· other· en· W6922812757 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2015
Typeother
Languageen
FieldSocial Sciences
TopicEducation Methods and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsStatistical parametric mappingGrey matterCortex (anatomy)Intraparietal sulcusSulcusCentral sulcusCorrelationStatistical analysisMotor cortex

Abstract

fetched live from OpenAlex

<p>The statistical parametric maps (shown in red yellow) were height-thresholded at p < 0.05 corrected for multiple comparisons using permutation-based non-parametric testing across space (FSL’s randomise tool, 5000 permutations). Increased grey matter volume was evident (A) in the bilateral supplementary motor area (SMA) and cingulate motor area (CMA) and (B) in the right primary motor and somatosensory cortex (MI and SI) and (C) in the left intraparietal sulcus (IPS). Age of handball training commencement was negatively correlated with grey matter volume in four small clusters, two clusters located in the right MI/SI region (D) and two clusters located in the left MI/SI region (E). The regions of interest subjected to the statistical analysis are shown in graded green. x, y, z represent coordinates of the Montreal neurological institute (MNI) stereotactic space.</p>

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.753
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.044
GPT teacher head0.297
Teacher spread0.253 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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