A public gene trap resource for mouse functional genomics
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 trapping is a high-throughput approach that can be used to introduce insertional mutations across the genome in mouse embryonic stem (ES) cells. Gene trap vectors simultaneously mutate and report the expression of the endogenous gene at the site of insertion and provide a DNA tag for the rapid identification of the disrupted gene. The generation of mutant mice from a large collection of ES cell lines carrying gene trap insertions could be applied to large-scale functional analysis of the 30,000 mammalian genes. The overall impact of gene trap resources will rest on the fraction of the genome that is accessible with this technology, the efficiency relative to other competing technologies and the availability of such a resource to the academic community. Lexicon Genetics, a US-based biotechnology company, was the first to implement a genome-wide gene trapping program 1 and has developed OmniBank (http://www.lexicongenetics.com), the largest library of mutant ES cell lines. A parallel effort was initiated in the public domain by several academic groups in the International Gene Trap Consortium (IGTC; http://www.igtc.ca). The recent release of OmniBank sequence tags to GenBank 2 has made it possible to compare the size and efficiency of the existing gene trap libraries.
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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.002 |
| 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