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Record W4393610770 · doi:10.5281/zenodo.10458911

White matter multi-scale dataset: Diffusion weighted MRI and synchrotron x-ray scans of vervet monkey-, healthy mouse-, and cuprizone mouse brains

2024· dataset· en· W4393610770 on OpenAlexaff
Hans Martin Kjer, Mariam Andersson, Yi He, Alexandra Pacureanu, Alessandro Daducci, Marco Pizzolato, Tim Salditt, Anna-Lena Robisch, Marina Eckermann, Mareike Toepperwien, Anders Bjorholm Dahl, Maria L. Elkjaer, Zsolt Illés, Maurice Ptito, Vedrana Andersen Dahl, Tim B. Dyrby

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typedataset
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsWhite matterSynchrotronDiffusion MRIAnatomyBiologyNuclear medicineMagnetic resonance imagingPhysicsMedicineOpticsRadiology

Abstract

fetched live from OpenAlex

Overall description: This repository contains the essential processed data files featured in this bioRxiv article. All the article figures are derived from these files. Additional files, e.g. intermediate processing steps, original reconstructions, or even additional samples can be shared upon request. Data format: All the shared files are stored in NIfTI (.nii) or compressed NIfTI (.nii.gz) format, to make it relatively straightforward to visually interact with them. We suggest using ITK-snap as the tool for inspection / interaction with the files. Many other NIfTI viewers will do the job 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.

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)0.001
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.040
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0040.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0400.067

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 head0.045
GPT teacher head0.317
Teacher spread0.272 · 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
GenreDataset

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
Published2024
Admission routes1
Has abstractyes

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