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Record W2886763311 · doi:10.1111/ejn.14101

Neuroscience without borders: Preserving the history of neuroscience

2018· editorial· en· W2886763311 on OpenAlexaff
Lorenzo Lorusso, Marco Piccolino, Saba Motta, Anna Gasparello, Jean‐Gaël Barbara, Laura Bossi‐Régnier, Gordon M. Shepherd, Larry W. Swanson, Pierre J. Magistretti, Barry J. Everitt, Zoltán Molnár, Richard E. Brown

Bibliographic record

VenueEuropean Journal of Neuroscience · 2018
Typeeditorial
Languageen
FieldNeuroscience
TopicNeurology and Historical Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNeurosciencePromotion (chess)PsychologyHistoryPolitical science

Abstract

fetched live from OpenAlex

Over the last 50 years, neuroscience has enjoyed a spectacular development, with many discoveries greatly expanding our knowledge of brain function. Despite this progress, there has been a disregard for preserving the history of these discoveries. In many European countries, historic objects, instruments, and archives are neglected, while libraries and museums specifically focusing on neuroscience have been closed or drastically cut back. To reverse this trend, the Federation of European Neuroscience Societies (FENS) has organized a number of projects, including (a) the History of Neuroscience online projects, (b) the European Brain Museum Project (EBM), (c) the History online library, (d) the FENS meeting History Corner, (e) history lectures in historic venues, and (f) a series of history seminars in various European venues. These projects aim to stimulate research in, and increase awareness of, the history of European neuroscience. Our seminars have attracted large audiences of students, researchers, and the general public, who have supported our initiatives for the preservation of the history of neuroscience for future generations and for the promotion of interest in the history of neuroscience. It is therefore urgent to develop new methods for preserving our history, not only in Europe but also in the rest of the world, and to increase greatly teaching and research in this important aspect of our scientific and cultural legacy.

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)0.004
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Open science, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.442
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.044
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.010
Scholarly communication0.0000.001
Open science0.0100.002
Research integrity0.0000.003
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 head0.056
GPT teacher head0.290
Teacher spread0.234 · 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
GenreEditorial

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

Citations9
Published2018
Admission routes1
Has abstractyes

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