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ABSTRACT Deficiencies in knowledge about water quality prevent or obscure progress on a panoply of public health problems globally. Specifically, such lack of information frustrates effective and efficient government regulation to protect the public from contaminated drinking water. In this Practical Paper, we lay out how recent scientific innovations in synthetic biology mean that rapid, at-home tests based on biosensor technology could be used to improve water quality monitoring and regulation, using the example of the U.S. Environmental Protection Agency's Lead and Copper Rule currently under revision. Biosensor tests can be used by non-scientists and the information that biosensor tests generate is relatively cheaper and faster than standard laboratory techniques. As such, they have the potential to make it possible to increase the number and frequency of samples tested. This, in turn, could facilitate more accurate compliance monitoring, justify more protective substantive standards, and more efficiently identify infrastructure priorities. Biosensors can also empower historically underrepresented communities by facilitating the visibility of inequities in lead exposure, help utilities to ensure safe water delivery, and guide policy for identifying and replacing lead-bearing water infrastructure, thereby improving public health. As the technology matures, biosensors have great potential to reveal water quality issues, thereby reducing public health burdens.
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Widespread use of National Academies consensus reports by the American public
In seeking to understand how to protect the public information sphere from corruption, researchers understandably focus on dysfunction. However, parts of the public information ecosystem function very well, and understanding this as well will help in protecting and developing existing strengths. Here, we address this gap, focusing on public engagement with high-quality science-based information, consensus reports of the National Academies of Science, Engineering, and Medicine (NASEM). Attending to public use is important to justify public investment in producing and making freely available high-quality, scientifically based reports. We deploy Bidirectional Encoder Representations from Transformers (BERT), a high-performing, supervised machine learning model, to classify 1.6 million comments left by US downloaders of National Academies reports responding to a prompt asking how they intended to use the report. The results provide detailed, nationwide evidence of how the public uses open access scientifically based information. We find half of reported use to be academic—research, teaching, or studying. The other half reveals adults across the country seeking the highest-quality information to improve how they do their job, to help family members, to satisfy their curiosity, and to learn. Our results establish the existence of demand for high-quality information by the public and that such knowledge is widely deployed to improve provision of services. Knowing the importance of such information, policy makers can be encouraged to protect it.
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- PAR ID:
- 10321940
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 119
- Issue:
- 9
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2107760119
