ILRI research on zoonoses in the Asia-Pacific region
Bibliographic record
Abstract
In terms of a broader picture of zoonoses and their impact on poor livestock keepers ILRI undertook a review for the Department for International Development UK in 2012. Initial Asia-based ILRI projects on zoonoses focused on highly pathogenic avian influenza H5N1 and were located in Indonesia. Efficacy of HPAI vaccination research was USAID-funded – with Government of Indonesia and FAO as partners (2007-09) and the other Department for International Development (DfID/UKAID 2007-10) implemented in conjunction with Government, Royal Veterinary College (UK) and International Food Policy Research Institute (IFPRI) – focused more on institutional challenges, and included a livelihood analysis, evaluation of risk management and communication and advocacy. The most substantial project implemented by ILRI in the Asia Pacific region with a focus on zoonoses was the Ecosystem Approaches to the Better Management of Zoonotic Emerging Infectious Diseases in SE Asia (EcoZD) project supported by IDRC, Canada. The project recently completed after a 5½ year project cycle. The core objective was to build capacity among researchers and other key stakeholders to utilise an EcoHealth approach in tackling a priority zoonosis(-es). During the first phase researchers and institutions were identified to form multi-disciplinary (trans-disciplinary) teams. More than just a series of training, teams were required to design, implement and write up the research, with mentoring by ILRI scientists and additional external experts.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".