Approaching ancient disease from a <scp>One Health</scp> perspective: Interdisciplinary review for the investigation of zoonotic brucellosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it