The Importance of Fostering and Funding Scientific Research, and its Relevance to Environmental Toxicology and Chemistry
Bibliographic record
Abstract
What do environmental contaminants and climate change have in common with the virus SARS-CoV-2 and the disease COVID-19? We argue that one common element is the wealth of basic and applied scientific research that provides the knowledge and tools essential in developing effective programs for addressing threats to humans and social-ecological systems. Research on various chemicals, including dichlorodiphenyltrichloroethane and per- and polyfluoroalkyl substances, resulted in regulatory action to protect environmental and human health. Moreover, decades of research on coronaviruses, mRNA, and recently SARS-CoV-2 enabled the rapid development of vaccines to fight the COVID-19 pandemic. In the present study, we explore the common elements of basic and applied scientific research breakthroughs that link chemicals, climate change, and SARS-CoV-2/COVID-19 and describe how scientific information was applied for protecting human health and, more broadly, socio-ecological systems. We also offer a cautionary note on the misuse and mistrust of science that is not new in human history, but unfortunately is surging in modern times. Our goal was to illustrate the critical role of scientific research to society, and we argue that research must be intentionally fostered, better funded, and applied appropriately. To that end, we offer evidence that supports the importance of investing in scientific research and, where needed, ways to counter the spread of misinformation and disinformation that undermines legitimate discourse. Environ Toxicol Chem 2023;42:581-593. © 2022 SETAC.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".