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Record W4224273251 · doi:10.31219/osf.io/h89js

NeuroLibre : A preprint server for full-fledged reproducible neuroscience

2022· preprint· en· W4224273251 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsUniversité de MontréalOttawa Regional Cancer FoundationInstitut Universitaire de Gériatrie de MontréalMontreal Heart InstituteMontreal Neurological Institute and HospitalPolytechnique Montréal
FundersCourtois FoundationFondation Brain CanadaMcGill University
KeywordsPreprintWorld Wide WebComputer scienceNarrativeCode (set theory)Data scienceArt

Abstract

fetched live from OpenAlex

NeuroLibre is a preprint server for neuroscience Jupyter Books, blending code, visualization and narrative text into one document. NeuroLibre archives the environment, code and data and also implements a technical review to ensure readers can reproduce the work. NeuroLibre offers an online platform where readers can reproduce or modify each preprint from a web browser, without any installation required. We hope that NeuroLibre will contribute to usher the research community in a new area of open and reproducible neuroscience. The preprint server is built with open source components, and can be freely adapted to meet the needs of other communities in the future as well.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.005
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 head0.022
GPT teacher head0.305
Teacher spread0.283 · 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

Quick stats

Citations30
Published2022
Admission routes2
Has abstractyes

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