Translating human genetics into mouse: The impact of ultra‐rapid <i>in vivo</i> genome editing
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
Gene-targeted mutant animals, such as knockout or knockin mice, have dramatically improved our understanding of the functions of genes in vivo and the genetic diversity that characterizes health and disease. However, the generation of targeted mice relies on gene targeting in embryonic stem (ES) cells, which is a time-consuming, laborious, and expensive process. The recent groundbreaking development of several genome editing technologies has enabled the targeted alteration of almost any sequence in any cell or organism. These technologies have now been applied to mouse zygotes (in vivo genome editing), thereby providing new avenues for simple, convenient, and ultra-rapid production of knockout or knockin mice without the need for ES cells. Here, we review recent achievements in the production of gene-targeted mice by in vivo genome editing.
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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) | 0.001 | 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