One health—Transdisciplinary opportunities for SETAC leadership in integrating and improving the health of people, animals, and the environment
Bibliographic record
Abstract
One Health is a collaborative, transdisciplinary effort working locally, nationally, and globally to improve health for people, animals, plants, and the environment. The term is relatively new (from ∼2003), and it is increasingly common to see One Health included by name in interinstitutional research partnerships, conferences, communications, and organizational frameworks, particularly those championed by the human health and veterinary medical communities. Environmental quality is arguably the least developed component within the One Health framework, but can be guided by expertise within the Society of Environmental Toxicology and Chemistry (SETAC). Despite SETAC's long history of tripartite (academic, government, business) interdisciplinary environmental science activities, the term "One Health" is seldom used in SETAC communications (i.e., many of SETAC's activities are guided by One Health, but it is called by other names in SETAC's journals, newsletters, and presentations). Accordingly, the objective of this Focus article is to introduce the One Health concept to the SETAC membership. The article discusses the origins, evolution, and utility of the One Health approach as an organizational framework and provides key examples of ways in which SETAC expertise can benefit the One Health community. The authors assert that One Health needs SETAC and, to be most effective, SETAC needs One Health. Given that One Health to date has focused too little on the environment, on ecosystems, and on contaminants, SETAC's constructive involvement in One Health presents an opportunity to accelerate actions that will ultimately better protect human and ecosystem health. Environ Toxicol Chem 2016;35:2383-2391. © 2016 SETAC.
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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 | 0.038 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.022 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.043 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".