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Record W2127618510

Consistencia epistémica del síndrome de Dificultades del Aprendizaje: aportaciones de la magnetoencefalografía como técnica de neuroimagen funcional* Epistemics for Learning Disabilities: Contributions from Magnetoencephalography, a Functional Neuroimaging Tool

2008· article· es· W2127618510 on OpenAlexaboutno aff
V Íctor Santiuste-Bermejo, Arta Santiuste-Díaz, Samuel A. Kirk

Bibliographic record

Venuenot available
Typearticle
Languagees
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetoencephalographyPsychologyNeuroimagingDyscalculiaHumanitiesCognitionCognitive psychologyDyslexiaNeuroscienceElectroencephalographyLinguisticsPhilosophyReading (process)
DOInot available

Abstract

fetched live from OpenAlex

The syndrome known as Learning Disabilities (LD) was described by S. Kirk in 1963. From that point on, institutions from the US, Canada and Spain have engaged in refining the concept and classification of LDs. The Com-plutense University in Spain, has proposed a descriptive and all-embracing definition, and has studied the different manifestations of LD, pursuing the description of biological markers and neurological features of LD’s main expressions: dyslexia, dyscalculia, dysorthographia, Attention Deficit and Hyperactivity Disorder –ADHD, and so forth. Findings in LD using functio-nal neuroimaging techniques, namely Magnetoencephalography (MEG), are described. MEG is a non-invasive technique, which records magnetic fields naturally generated by the brain and their spatial distribution. It allows simultaneous functional and structural information. MEG is therefore used in the study of primary and superior cognitive functions, in surveillance of patterns of normal cognitive function and those specific to the different LD clinical manifestations.

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)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0040.005
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.030
GPT teacher head0.281
Teacher spread0.251 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2008
Admission routes1
Has abstractyes

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