Anti Microbial Resistance in Salmonella
Bibliographic record
Abstract
Antibiotics are one of the major drugs to eradicate microbial infection. Many types of antibiotics have been used as therapeutics in several fields such as medical, agriculture, animal husbandry for human beings as well as animals. In past few years microbes have become resistant to some common antibiotics. We found that drug resistance is escalating at an alarming rate. Some of the infections like typhoid, pneumonia, tuberculosis, and gonorrhea are becoming difficult to treat while antibiotics are becoming less effective. Typhoid fever is one of the most common foodborne illnesses leading to many deaths annually worldwide. The emergence of multi-drug resistant Salmonella enterica serovar Typhi strains (S. Typhi) has resulted in several large outbreaks of enteric fever in many developing countries of the world leading to increased morbidity and mortality. Multi-drug resistance remains a major public health problem, particularly in developing countries of Asia and Africa. Some important measures like rational use of antibiotics, improvement in public sanitation facilities, availability of clean drinking water, promotion of safe food handling practices and public health education can play a crucial role in the prevention of multiple drug resistant typhoid fever.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".