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Record W4386560484 · doi:10.1038/s41418-023-01215-z

RUNDC1 inhibits autolysosome formation and survival of zebrafish via clasping ATG14-STX17-SNAP29 complex

2023· article· en· W4386560484 on OpenAlexaff
Rui Zhang, Yuyan Yang, Chao He, Xin Zhang, Vincenzo Torraca, Shen Wang, Nan Liu, Jiaren Yang, Shicheng Liu, Jinglei Yuan, Dongzhi Gou, Li Shi, Xueying Dong, Yufei Xie, Junling He, Hua Bai, Mengyu Hu, Zhiquan Liao, Yuan Huang, Hao Lyu, Shuai Xiao, Dong Guo, Declan W. Ali, Marek Michalak, Cong Ma, Xing‐Zhen Chen, Jingfeng Tang, Cefan Zhou

Bibliographic record

VenueCell Death and Differentiation · 2023
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of Alberta
FundersWuhan Science and Technology ProjectHubei UniversityHubei University of TechnologyNational Natural Science Foundation of China
KeywordsAutophagyCell biologyZebrafishAutophagosomeChemistryPhosphorylationBiologyBiochemistryApoptosisGene

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)0.000
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.030
GPT teacher head0.266
Teacher spread0.235 · 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

Citations41
Published2023
Admission routes1
Has abstractno

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