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Record W6893014435 · doi:10.5281/zenodo.13123028

PyBIDS: Python tools for BIDS datasets

2024· other· en· W6893014435 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typeother
Languageen
FieldComputer Science
TopicMobile Crowdsensing and Crowdsourcing
Canadian institutionsCentre for Addiction and Mental HealthUniversité de MontréalOttawa HospitalUniversité de SherbrookeUniversity of TorontoMontreal Neurological Institute and HospitalPolytechnique MontréalWestern UniversityConcordia UniversityHolland Bloorview Kids Rehabilitation HospitalMcGill University
Fundersnot available
KeywordsPython (programming language)Information systemMIT LicenseScripting languageSoftware

Abstract

fetched live from OpenAlex

PyBIDS is a Python library to centralize interactions with datasets conforming BIDS (Brain Imaging Data Structure) format. For more information about BIDS visit bids.neuroimaging.io.

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)0.005
metaresearch head score (Gemma)0.013
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: Software · Consensus signal: none
Teacher disagreement score0.151
Threshold uncertainty score0.505

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0040.005
Open science0.0050.006
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.1510.144

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 head0.044
GPT teacher head0.265
Teacher spread0.221 · 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
GenreSoftware

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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