MétaCan
Menu
Back to cohort
Record W4394285370 · doi:10.6084/m9.figshare.14728552

Additional file 1 of Transcriptional dynamics of transposable elements when converting fibroblast cells of Macaca mulatta to neuroepithelial stem cells

2021· dataset· en· W4394285370 on OpenAlexaff
Dahai Liu, Li Liu, Kui Duan, Junqiang Guo, Shipeng Li, Zhigang Zhao, Xiaotuo Zhang, Nan Zhou, Yun Zheng

Bibliographic record

VenueOpen MIND · 2021
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsNeuroepithelial cellBiologyStem cellTransposable elementCell biologyDynamics (music)FibroblastGeneticsCell cultureNeural stem cellPhysicsGeneGenome

Abstract

fetched live from OpenAlex

Additional file 1 Supplementary Table S1. The expression levels of 1627 dynamically expressed genes in the reprogramming procedure of rhesus monkey fibroblast cells toward neuroephithelia stem cells. Supplementary Table S2 to S17. The enriched GO terms of genes in Cluster G0 to G15 of Figure 1A, respectively. Supplementary Table S18. The expression levels of 495 dynamically expressed TEs in the reprogramming procedure of rhesus monkey fibroblast cells toward neuroephithelia stem cells. Supplementary Table S19. The expression levels of 98 dynamically expressed LTRs in the reprogramming procedure of rhesus monkey fibroblast cells toward neuroephithelia stem cells. Supplementary Table S20. The 13 MacERV3 LTRs that were selected for PCR verification. Supplementary Table S21. The primers used in this study.

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)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.424
Threshold uncertainty score0.822

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.004
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.4240.118

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 head0.226
Teacher spread0.201 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOpen MINDSame topicChromosomal and Genetic VariationsFrench-language works237,207