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Record W4246416856 · doi:10.1177/0963721416628530

Neuroenergetics

2016· article· en· W4246416856 on OpenAlexaff
Peter R. Killeen, Vivienne A. Russell, Rosemary Tannock

Bibliographic record

VenueCurrent Directions in Psychological Science · 2016
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsPsychologyContext (archaeology)Energy (signal processing)Cognitive psychologyLatency (audio)NeuroscienceComputer scienceStatistics

Abstract

fetched live from OpenAlex

The brain uses 20% of the body’s energy. The processes delivering that energy to neurons can fail in numerous ways. The neuroenergetics theory draws out the implications of failure in the supply chain between blood capillaries and neurons. The theory is implemented as a diffusion model that yields response-latency distributions, error rates, and other predictions for typical individuals engaged in focused activities and for special populations such as those with neurodevelopmental disorders. It predicts the effects of stimulants, trial spacing, and fatigue. Here, the implications of energetic insufficiency are explored in the context of the positive manifold of abilities, disabilities, and psychiatric comorbidities.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.103
GPT teacher head0.457
Teacher spread0.354 · 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 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

Citations15
Published2016
Admission routes1
Has abstractyes

Explore more

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