Approaching ancient disease from a <scp>One Health</scp> perspective: Interdisciplinary review for the investigation of zoonotic brucellosis
Bibliographic record
Abstract
Abstract Today, brucellosis is the most common global bacterial zoonosis, bringing with it a range of significant health and economic consequences, yet it is rarely identified from the archaeological record. Detection and understanding of past zoonoses could be improved by triangulating evidence and proxies generated through different approaches. The complex socioecological systems that support zoonoses involve humans, animals, and pathogens interacting within specific environmental and cultural contexts, and as such, there is a diversity of potential datasets that can be targeted. To capture this, in this paper, we consider how to approach the study of zoonotic brucellosis in the past from a One Health perspective, one which explicitly acknowledges the health link between people, animals, and environments (both physical and cultural). One Health research is explicitly interdisciplinary and conceptually moves away from an anthropocentric approach, allowing the component parts to be considered in holistic and integrated ways to deliver more comprehensive understanding. To this end, in this paper, we review the methods, selected evidence, and potential for past brucellosis identification and understanding, focussing on osteological markers in humans and animals, historical, biomolecular, and epidemiological approaches. We also present an agenda and potential for future research.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".