Protocol for developing a Database of Zoonotic disease Research in India (DoZooRI)
Bibliographic record
Abstract
INTRODUCTION: Zoonotic and emerging infectious diseases (EIDs) represent a public health threat that has been acknowledged only recently although they have been on the rise for the past several decades. On an average, every year since the Second World War, one pathogen has emerged or re-emerged on a global scale. Low/middle-income countries such as India bear a significant burden of zoonotic and EIDs. We propose that the creation of a database of published, peer-reviewed research will open up avenues for evidence-based policymaking for targeted prevention and control of zoonoses. METHODS AND ANALYSIS: A large-scale systematic mapping of the published peer-reviewed research conducted in India will be undertaken. All published research will be included in the database, without any prejudice for quality screening, to broaden the scope of included studies. Structured search strategies will be developed for priority zoonotic diseases (leptospirosis, rabies, anthrax, brucellosis, cysticercosis, salmonellosis, bovine tuberculosis, Japanese encephalitis and rickettsial infections), and multiple databases will be searched for studies conducted in India. The database will be managed and hosted on a cloud-based platform called Rayyan. Individual studies will be tagged based on key preidentified parameters (disease, study design, study type, location, randomisation status and interventions, host involvement and others, as applicable). ETHICS AND DISSEMINATION: The database will incorporate already published studies, obviating the need for additional ethical clearances. The database will be made available online, and in collaboration with multisectoral teams, domains of enquiries will be identified and subsequent research questions will be raised. The database will be queried for these and resulting evidence will be analysed and published in peer-reviewed journals.
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 | 0.133 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.210 | 0.044 |
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".