Salmonella - Perspectives for Low-Cost Prevention, Control and Treatment
Bibliographic record
Abstract
Salmonella is a Gram-negative bacterium and a member of the Enterobacteriaceae family that causes infections in humans and animals, making it one of the most common causes of bacterial gastroenteritis worldwide. Since its discovery in the late 1800s, significant progress has been made in the understanding of its genetics, classification, pathogenesis, detection, prevention, control, and treatment. Numerous reviews and chapters on Salmonella have been published, but some gaps remain to be addressed. This book includes seven chapters that focus on the low-cost prevention, control, and treatment of salmonellosis in developing countries. It begins with a brief review of Salmonella, followed by chapters on the transmission of the organism in food and companion animals relevant to the One Health approach, CRISPR-Cas systems in Salmonella for pathogen typing in diagnosis and surveillance, the low-cost control of Salmonella using solar disinfection of water in resource-limiting communities, and transmission and antimicrobial resistance (AMR) in Salmonella across the One Health sector. This book also introduces a new concept of AMR reversal using traditional Chinese medicine. The information provided in this book will encourage Salmonella researchers, medical professionals, and students to further enhance their own research and education as well as encourage new researchers to include Salmonella in their future research initiatives.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.017 |
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".