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Record W2324812806 · doi:10.4161/auto.27749

Seeing is believing

2014· article· en· W2324812806 on OpenAlexaff
Fangming Lin, Zhao V. Wang, Joseph A. Hill

Bibliographic record

VenueAutophagy · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRenal and related cancers
Canadian institutionsColumbia College
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood Institute
KeywordsAutophagyBiologyTransgenePI3K/AKT/mTOR pathwayKidneyKidney diseaseCell biologyGenetically modified mouseCancer researchGeneApoptosisGeneticsSignal transductionEndocrinology

Abstract

fetched live from OpenAlex

Ischemic injury to the kidneys is a prevalent clinical problem, contributing importantly to chronic kidney disease. Yet, underlying molecular mechanisms are elusive. To address the possible role of autophagy, we engineered a novel strain of mice harboring a ubiquitously expressed CAG-RFP-EGFP-LC3 transgene. Using this tool, we examined the post-ischemic kidney and detailed the dynamics of renal tubular epithelial autophagy. In addition, we defined the role of MTOR in the resolution of autophagy during epithelial survival and kidney repair.

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)0.002
metaresearch head score (Gemma)0.010
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.041
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.009
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0410.018

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 head0.003
GPT teacher head0.208
Teacher spread0.205 · 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
GenreCommentary

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

Citations26
Published2014
Admission routes1
Has abstractyes

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