Recomendación 1. Importancia del nexo biodiversidad y salud para los planes y estrategias nacionales de biodiversidad–priorización de la prevención primaria en salud
Bibliographic record
Abstract
The concept of “One Health” summarizes an idea that has been known for more than a century: human, animal and ecosystem health are interconnected and interdependent (World Organization for Animal Health [OIE], 2024). Wild species harbor a wide diversity of pathogens, to which humans may be susceptible. However, it is critical to consider that human pathogens represent only a small fraction of the world's parasite diversity (Balloux and van Dorp, 2017). It is important to urgently address the drivers of biodiversity loss in order to reduce health risks, as suggested by the Kunming-Montreal Global Biodiversity Framework (Subsidiary Body on Scientific, Technical and Technological Advice, 2024). Human activities have crossed natural barriers in an unsustainable manner, invading wild areas or extracting specimens for consumption, trade (pets), or as derived or manufactured products, situations that facilitate the risk of unsafe contact between animals and humans. Hunting for different purposes is often associated with logging activities, during which there is a risk of direct contact with animals and arthropod vectors of pathogens (Eve et al., 2000). Similarly, mining facilitates indirect events and new infections (Ellwanger et al., 2020). These habitat interventions alter the dynamics and natural interactions of wild species and their environment, increasing direct contact with humans and increasing the likelihood of zoonotic disease transmission.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.029 | 0.012 |
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".