Editorial: Rising stars in infectious agents and disease: 2021
Bibliographic record
Abstract
Amongst the different pathogenic microorganisms described in animals and humans, 21 viruses show a huge diversity and their complex interactions with their host are often 22 challenging to study, which is limiting the development of prophylactic and therapeutic 23 treatments. Since the emergence of African swine fever (ASF) in China in 2018, the disease 24 represents an increasing economic burden in many countries. The causing virus (ASFV), 25 already an old issue, has been identified in 1921 in Kenya (Eustace Montgomery, 1921) and so 26 far vaccines are still missing. Many strains of ASFV have been described and this large DNA 27 virus developed complex interactions with innate and adaptive immune systems. Also 28 responsible of major economic losses in pig production, the porcine reproductive and 29 respiratory syndrome virus (PRRSV) also represents a strong challenge for vaccine 30 development (Lunney et al., 2016). Indeed, there are a plethora of strains and vaccines would 31 need to protect against various and constantly evolving PRRSV strains. Then, once safe and 32 effective vaccines are ultimately developed, the story is not ended and surveillance started to
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.043 | 0.041 |
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".