Bibliographic record
Abstract
Vaccines and immunization programs can play a key role in addressing the growing challenge of antimicrobial resistance (AMR).Amongst the high priority vaccines in development are several AMR pathogens, including: Clostridium difficile, Staphylococcus aureus, Streptococcus pneumoniae, Mycobacterium tuberculosis and Neisseria gonorrhoeae.There is evidence that vaccination can reduce the prevalence of AMR microbes, as demonstrated by both pneumococcal and Haemophilus influenza b vaccines.Research continues on many vaccine-preventable diseases, many of these AMR pathogens, including HIV and universal influenza vaccines.Not only do vaccines prevent infections, they can also prevent secondary opportunistic infections from AMR microbes-for example, bacterial pneumonia following influenza infections.The reduced need to treat these opportunistic infections would also mitigate the advance of AMR microbes in our communities.However, vaccines are not a panacea.One downside to the use of vaccines to address AMR is vaccine hesitancy, which undermines efforts to achieve herd immunity, but this is being increasingly addressed by public health education campaigns.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".