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
The health of humans, domestic and wild animals, plants, and the environment are inter-dependent. Global anthropogenic change is a key driver of disease emergence and spread and leads to biodiversity loss and ecosystem function degradation, which are themselves drivers of disease emergence. Pathogen spill-over events and subsequent disease outbreaks, including pandemics, in humans, animals and plants may arise when factors driving disease emergence and spread converge. One Health is an integrated approach that aims to sustainably balance and optimize human, animal and ecosystem health. Conventional disease surveillance has been siloed by sectors, with separate systems addressing the health of humans, domestic animals, cultivated plants, wildlife and the environment. One Health surveillance should include integrated surveillance for known and unknown pathogens, but combined with this more traditional disease-based surveillance, it also must include surveillance of drivers of disease emergence to improve prevention and mitigation of spill-over events. Here, we outline such an approach, including the characteristics and components required to overcome barriers and to optimize an integrated One Health surveillance system.
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.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it