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Record W4283013740 · doi:10.31222/osf.io/4nvpt

EEG and ERP Methods - Preregistration Template

2022· preprint· en· W4283013740 on OpenAlexaff
Gisela Hanna Govaart, Antonio Schettino, Saskia Helbling, David M. A. Mehler, William Xiang Quan Ngiam, David Moreau, Francesco Chiossi, Anthony P. Zanesco, Yufang Yang, Rémi Gau, James E. Bartlett, José C. García Alanis, Jennifer N. Gutsell, Melis Çetinçelik, Yuri G. Pavlov, Аnđela Šoškić, Benedikt Ehinger, Pedram Mouseli, Johannes Algermissen, Marta Topor, Jose Pérez‐Navarro, Greta Häberle, Melanie Steffi Schreiner, Katrin Rothmaler, Guiomar Niso, Mariella Paul

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsElectroencephalographyEvent-related potentialEvent (particle physics)Computer scienceWishSpace (punctuation)PsychologySociologyNeurosciencePhysics

Abstract

fetched live from OpenAlex

This preregistration template guides researchers who wish to preregister their EEG projects, more specifically studies investigating event-related potentials (ERPs) in the sensor space.

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)0.003
metaresearch head score (Gemma)0.023
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: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.068
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0680.054

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 head0.093
GPT teacher head0.394
Teacher spread0.301 · 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
GenreProtocol

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

Citations18
Published2022
Admission routes1
Has abstractyes

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