Toxicology, environmental chemistry, ecotoxicology, and One Health: definitions and paths for future research
Bibliographic record
Abstract
The definitions of toxicology, environmental toxicology, environmental chemistry, environmental risk, and ecotoxicology are closely related and sometimes used as synonyms, whereas One Health is a more recent, complementary concept. This contribution examines the origins of the usages of these terms, explores their interchangeability (whether appropriate or not), and proposes some paths to better define each. The usage of these terms is evolving, and current research and paradigms are progressing toward the integration of broader, more integrative perspectives, such as the One Health approach. One Health is a holistic approach that helps link and integrate work on environmental and human health impacts. Definitions and research should not necessarily strive to segregate human vs. environmentally focused work, and most of the problems are complex and interconnected. Future research endeavors and funding programs must better reflect the multidisciplinary nature of environmental toxicology, and more broadly, One Health research and environmental research must recognize the interrelationships of human health, environmental health, ecotoxicology, and a multitude of geochemical, microbiological, and ecological processes.
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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 | 0.101 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.007 | 0.108 |
| Scholarly communication | 0.025 | 0.070 |
| Open science | 0.007 | 0.017 |
| Research integrity | 0.013 | 0.033 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".