Faculty Opinions recommendation of Transcriptionally active HERV-H retrotransposons demarcate topologically associating domains in human pluripotent stem cells.
Bibliographic record
Abstract
designed and supervised the experiments, analysis, and data interpretation.Y.Z.implemented the analysis pipeline and analyzed all sequencing datasets, interpreted the results, designed the experiments for HERV-H functional studies.T.L. generated the CRISPR-Cas9-edited cell lines for HERV-H functional studies, performed differentiation and qPCR of the corresponding cell lines.S.P. performed the Hi-C experiments for all stages of cardiomyocyte differentiation and helped with interpretation of the results.M.A. analyzed the HERV-H knock-in data with help from Y.Q.regarding allelic analysis.J.G. and E.N.F.performed cell culture, differentiation and collected cells for Hi-C, ChIP-seq and RNA-seq assays.E.D. contributed to analysis and interpretation of the ChIPseq data.R.H. performed the Hi-C experiments for HERV-H knock-out, CRISPRi, HERV-H knock-in and primate iPSC cell lines.ChIP-seq experiments were performed by A.Y.L. (H3K27ac), S.C. (CTCF), Q.Z. and H.H. (SMC3).Y.Q. and R.F. helped with the analysis of Hi-C datasets.K.M. helped with the genome editing experiments.L.Y., J.C.I.B. and J.W. cultured and prepared non-human primate iPSCs for sequencing and interpreted data.Z.Y. performed the RNA-seq experiments.S.M.E.helped with interpretation of the results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.052 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".