The necessity for carnivore experimentation in predicting the next zoonotic disease epidemic
Bibliographic record
Abstract
Zoonoses are human infections or diseases caused by disease spillover from vertebrate animals to people [1]. Spillover is the movement of pathogens from their normal host to a novel species [2]; this can occur through bodily fluids, bites, food, water, or contact with surfaces where infected animals have travelled [3]. Although some zoonoses remain established within populations and primarily affect only one person per spillover (classified as enzootic zoonoses—e.g., rabies), others can be transmitted between people and result in localized, or even global outbreaks [4]. Zoonoses account for over 60% of infectious diseases in humans [4] and can be caused by viruses, parasites, bacteria, or fungi. Of these, viral zoonoses prove to be of greatest detriment to the public on a widespread scale, as they are responsible for numerous epidemics and pandemics, including severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and the novel coronavirus (COVID-19) [5-7]. Research has also been conducted on different taxonomic orders of species, such as Carnivora — placental animals which obtain nutrients from flesh — and their viral spillover risk [11].
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".