Editorial: New-generation vaccines and novel vaccinal strategies against infectious diseases of livestock, wild and companion animals
Bibliographic record
Abstract
Vaccination against infectious disease is an invaluable tool to protect humans against severe morbidity 21 and mortality. For this reason, significant advances to human vaccines have propelled the field of 22 vaccinology forward. Emerging and neglected diseases still pose an important challenge [1], fortunately 23 the evolution of technology in the vaccinology field is providing modern options to successfully prevent 24 viral and non-viral human infections [2, 3]. In contrast, development of animal vaccines has lagged, 25 although their importance is just as critical to the health and welfare of wild, domestic and companion 26 animals. In addition to the zoonotic risk it poses to public health, infectious animal diseases have 27 accounted for more than 20 billion euros in direct losses over the last decade, and more than ten times We would like to extend our thanks to all the authors who participated in this Research Topic 96 and sincere gratitude to all the reviewers and the Frontiers team for their hard work on this 97 compellation. 98 99 References: 100
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 0.020 |
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".