Bibliographic record
Abstract
Streptococcus suis is considered among the top bacterial pathogens leading to important economic losses to the swine industry, with the incidence of disease increasing as the prophylactic use of antimicrobial is being vanished worldwide. S. suis is also a zoonotic agent afflicting people in close contact with infected pigs or pork meat. Besides, in some Asian countries, it is considered a major public health concern for the general population as well. Antimicrobial resistance is one of the most important global health challenges, and in the absence of preventive measures (such as effective vaccines), S. suis remains a risk for increased antimicrobial resistance and transmission of resistance genes to other bacteria beyond the host animal species. The studies in this Special Issue have evidenced the importance of swine population demographics and management on disease control, progress in molecular tools to better understand the epidemiology of S. suis infections in swine and humans, and the mechanisms involved in different aspects of the immuno-pathogenesis of the disease. The importance of reducing the prophylactic use of antimicrobials in livestock productions and the development of alternative control measures, including vaccination, are herein discussed.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".