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Record W7135370260 · doi:10.14339/sto-tr-hfm-311

Neuroenhancement in Military Personnel::Conceptual and Methodological Promises and Challenges

2023· report· en· W7135370260 on OpenAlexfundno aff
Jan B. F. van Erp, Monique E. Beaudoin, Tad T. Brunyé, Kathryn A. Feltman, Hal S. Greenwald, Kristin J. Heaton, Richard A. McKinley, Oshin Vartanian, Annika Vergin, Annalise Whittaker, Brock Wester, Korine A. Ohiri, Leslie Hamilton

Bibliographic record

VenueUniversity of Twente Research Information · 2023
Typereport
Languageen
Field
Topic
Canadian institutionsnot available
FundersAdvanced Research Projects AgencyDefense Advanced Research Projects AgencyMinistère de la Défense NationaleU.S. Department of Defense
KeywordsCognitionField (mathematics)Face (sociological concept)NeuromodulationMilitary personnel

Abstract

fetched live from OpenAlex

Military personnel face harsh conditions that strain their physical and mental well-being, depleting resources necessary for sustained operational performance. Future operations will impose even greater demands on soldiers in austere environments with limited support, and new training and technological approaches are essential. This report highlights the progress in cognitive neuroenhancement research, exploring techniques such as neuromodulation and neurofeedback, and emphasizes the inherent challenges and future directions in the field of cognitive neuroenhancement for selection, training, operations, and recovery.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.718
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.624
GPT teacher head0.442
Teacher spread0.182 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

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Same venueUniversity of Twente Research InformationFrench-language works237,207