Using the CRISPR-Cas System to Solve Porcine Viral Infection–related Issues
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
Viral infection-associated diseases seriously affect the development of the swine industry and pose a potential threat to the health of humans. Fortunately, the emergence of CRISPR-Cas has inspired scientists' efforts to address these viral-related issues in pigs using this technology. Based on progress in the field to date, this review summarizes the applications of the CRISPR-Cas system in dissecting the functions of swine viral genes and host factors related to their infections, improving the antiviral ability of pigs, inactivating porcine endogenous retrovirus prior to xenotransplantation, and detecting swine viruses. We also discuss the challenges of the practice of porcine genetic modification and the CRISPR-Cas system's prospects as an important tool for basic virology research and a promising strategy for controlling swine viral infection-related diseases.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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