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Record W3019435489 · doi:10.1016/j.cels.2020.03.003

BraInMap Elucidates the Macromolecular Connectivity Landscape of Mammalian Brain

2020· article· en· W3019435489 on OpenAlexafffund
Mohammad Reza Pourhaghighi, Peter E.A. Ash, Sadhna Phanse, Florian Goebels, Lucas ZhongMing Hu, Siwei Chen, Yingying Zhang, Shayne D. Wierbowski, Samantha Boudeau, Mohamed Taha Moutaoufik, Ramy Malty, Edyta Małolepsza, Kalliopi Tsafou, Aparna Nathan, Graham L. Cromar, Hongbo Guo, Ali Al Abdullatif, Daniel J. Apicco, Lindsay A. Becker, Aaron D. Gitler, Stefan M. Pulst, Ahmed Youssef, Ryan Hekman, Pierre C. Havugimana, Carl A. White, Benjamin C. Blum, Antonia Ratti, Camron D. Bryant, John Parkinson, Kasper Lage, Mohan Babu, Haiyuan Yu, Gary D. Bader, Benjamin Wolozin, Andrew Emili

Bibliographic record

VenueCell Systems · 2020
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsHospital for Sick ChildrenSickKids FoundationUniversity of ReginaUniversity of Toronto
FundersNational Institute on AgingNational Institute of Environmental Health SciencesCanadian Institutes of Health ResearchALS Society of CanadaNational Human Genome Research InstituteBoston UniversityBrightFocus FoundationNational Institutes of HealthNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesAlzheimer's Association
KeywordsInteractomeNeuroscienceComputational biologyBiologyAmyotrophic lateral sclerosisRNA splicingBrain functionFunction (biology)Cell biologyRNADiseaseGeneticsGeneMedicine

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 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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.257
Teacher spread0.232 · 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 designBench or experimental
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

Citations86
Published2020
Admission routes2
Has abstractno

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