DISCONTOOLS supplement: Current research gaps for advancing control of infectious diseases in production animals
Bibliographic record
Abstract
DISCONTOOLS (DISease CONtrol TOOLS) is an open-access database to assist public and private funders of animal health research in identifying research gaps and planning future research. This database is supported by a range of national funders of animal health research in Europe, with industry providing secretariat support. Information in the database is generated by disease-specific expert groups. In this DISCONTOOLS Supplement, contributing experts expanded their gap analyses into review papers for 15 diseases, covering zoonotic, production and epizootic diseases. Across this diverse array of diseases, it is clear that fundamental research on host-pathogen relationships and immune responses remains critical for evidence-based development of novel vaccines, pharmaceuticals and diagnostics to improve animal health. Furthermore, it is also obvious that there is need to better utilize economics and knowledge regarding "human factors" to optimise uptake and use of a broad range of tools and insights.
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 | 0.013 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.250 | 0.069 |
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".