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Record W4226126264 · doi:10.1007/s12311-021-01353-5

Cerebello-cerebral Functional Connectivity Networks in Major Depressive Disorder: a CAN-BIND-1 Study Report

2022· article· en· W4226126264 on OpenAlexafffund
Sheeba Arnold Anteraper, Xavier Guell, Yoon Ji Lee, Jovicarole Raya, Ilya Demchenko, Nathan W. Churchill, Benício N. Frey, Stefanie Hassel, Raymond W. Lam, Glenda MacQueen, Roumen Milev, Tom A. Schweizer, Stephen C. Strother, Susan Whitfield‐Gabrieli, Sidney H. Kennedy, Venkat Bhat

Bibliographic record

VenueThe Cerebellum · 2022
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsBaycrest HospitalUniversity of TorontoProvidence Health CareSt. Joseph’s Healthcare HamiltonUniversity of British ColumbiaQueen's UniversityMcMaster UniversityUniversity of CalgarySt. Michael's Hospital
FundersOntario Brain Institute
KeywordsNeuroscienceDefault mode networkCerebellumMajor depressive disorderPsychologyResting state fMRINeuroimagingFunctional magnetic resonance imagingNeurologyConnectomeCytoarchitectureFunctional connectivityFunctional neuroimagingCognition

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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 designObservational
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

Citations3
Published2022
Admission routes2
Has abstractno

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