Bibliographic record
Abstract
The next line of defence against antibiotic-resistant bacteria could be genetically modified viruses. If bacteriophage, viruses that replicate by infecting bacterial cells, are engineered with genetic programing to target bacteria that harm humans, a large amount of time and money could be saved by no longer adapting antibiotics to evolving bacteria. Using phage as a form of medicine is called Phage Therapy. But that is not the only application for phage technology, phage can be adapted to initiate cell death through ge¬netic codes, or gene expression for cells that do not use certain beneficial genes. Conventional antibiotics affect more types of cells than are necessary to cure illnesses and thus produce harmful side effects. Phage therapy can be used to sterilize food, treat infection, stimulate gene expression, and perhaps much more. La prochaine ligne de défense contre les bac¬téries résistantes aux antibiotiques pourrait être les virus génétiquement modifiés. Si la bacté¬riophage, un virus qui se répliquent en infectant des cellules bactériennes, sont conçus avec la programmation génétique pour cibler les bacté¬ries qui infectent humains, beaucoup de temps et d'argent pourrait être économisé en n'adaptant plus les antibiotiques a l'évolution des bactéries. On s'appele cette utilization medicinale de phage «Phagothérapie». Mais ce n'est pas la seule ap¬plication de la technologie de phage, ca peut être adapté pour déclencher la mort des cellules par des codes génétiques ou par l'expression des gènes des cellules qui n'utilisent pas de certains gènes bénéfiques. Antibiotiques classiques af¬fectent plusieurs types de cellules, plus qui est nécessaire pour guérir les maladies, et donc ils produisent les effets secondaires néfastes. Phage thérapie peut être utilisé pour la stérili¬sation d'aliments, traiter des infections, stimuler l'expression du gène, et peut-être beaucoup plus.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".