Bacteriophages, a Solution to the Destruction of Antibiotic-Resistant Bacteria
Bibliographic record
Abstract
Bacteriophage is the generic name given to microorganisms that can destroy bacteria. The name (which comes from the Latin bacteria and the Greek φᾰγεῖν phagein - "to eat", "to devour") was introduced by the Canadian bacteriologist Félix d'Herelle in 1917, the year he discovered a virus possessing such characteristics. Also called bacteria-eating viruses, bacteriophages represent a group of viruses with a destructive effect on bacteria (lytic effect), being widespread in nature, in all living environments (water, soil, air). Discovered during the First World War and developed in the 1920s and 1930s, phage therapy is currently making a comeback and enjoying a renewed interest in countries such as the USA, Belgium and France, in parallel with the development of antibiotic resistance, in the face of the increasing resistance of bacteria to antibiotics, a challenge for the whole planet. Today, phage therapy has begun to be used to disinfect food. Also, before surgery, in some hospitals, bacteriophages are sprayed in the operating rooms to reduce the risk of infection. Through their mechanism of action, bacteriophages can become a kind of additional immune system for organisms, and phage therapy, a therapy to be studied and perspective for maintaining human and animal health.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".