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Record W2610534091 · doi:10.15173/m.v1i26.949

Big Brain Imaging: A Quest to Map the Most Complex Organ - The Human Brain

2014· article· en· W2610534091 on OpenAlexvenueno aff
Tony Zhang

Bibliographic record

VenueThe Meducator · 2014
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNeuroscienceNeuroimagingHuman brainMedicinePsychology

Abstract

fetched live from OpenAlex

In the past several decades, brain-mapping has emergedas a revolutionary process to further understand therelationship between neural structure and function.1 Withincreased computing power, computer-mapping in threedimensionalstandard brain atlases can now be producedat nearly cellular resolution. The recent commencement ofone of the world’s largest scientific projects to date, theHuman Brain Project (HBP), aims to synthesize massivedata generated by research institutions across Europe.Forschungszentrum Jülich (Research Centre Juelich), amember of the HBP, currently has the highest resolution3D brain atlas in the world, the BigBrain, at 20-micrometerresolution.2 The author outlines the significance ofobserver-independent mapping and the different visualand objective techniques used to map the primary motorcortex.

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)0.002
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.499
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.054
GPT teacher head0.306
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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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