Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The human genome must be tightly packaged in order to fit inside the nucleus of a cell. Genome organization is functional rather than random, which allows for the proper execution of gene expression programs and other biological processes. Recently, three-dimensional chromatin organization has emerged as an important transcriptional control mechanism. For example, enhancers were shown to regulate target genes by physically interacting with them regardless of their linear distance and even if located on different chromosomes. These chromatin contacts can be measured with the "chromosome conformation capture" (3C) technology and other 3C-related techniques. Given the recent innovation of 3C-derived approaches, it is not surprising that we still know very little about the structure of our genome at high-resolution. Even less well understood is whether there exist distinct types of chromatin contacts and importantly, what regulates them. A new form of regulation involving the expression of long non-coding RNAs (lncRNAs) was recently identified. lncRNAs are a very abundant class of non-coding RNAs that are often expressed in a tissue-specific manner. Although their different subcellular localizations point to their involvement in numerous cellular processes, it is clear that lncRNAs play an important role in regulating gene expression. How they control transcription however is mostly unknown. In this review, we provide an overview of known lncRNA transcription regulation activities. We also discuss potential mechanisms by which ncRNAs might exert three-dimensional transcriptional control and what recent studies have revealed about their role in shaping our genome.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it