Evidence map›Paper›PMID 38271507›Full record

ArticleScience (New York, N.Y.)2024

Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

Simon Greenhill, Hannah Druckenmiller, Sherrie Wang, David A Keiser, Manuela Girotto, Jason K Moore, Nobuhiro Yamaguchi, Alberto Todeschini, Joseph S Shapiro

Abstract read
In one paragraph

Article in Science (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Simon GreenhillDepartment of Agricultural and Resource Economics, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0000-0001-9726-2477
Hannah Druckenmiller *Resources for the Future, Washington, DC 20036, USA.ORCID 0000-0003-4605-1172
Sherrie Wang *Goldman School of Public Policy, University of California, Berkeley, Berkeley, CA 94720, USA.
David A KeiserDepartment of Resource Economics, University of Massachusetts, Amherst, Amherst, MA 010013, USA.ORCID 0000-0002-4687-5633
Manuela GirottoDepartment of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA 94720, USA.
Jason K MooreUS Department of Energy, Washington, DC 20585, USA.ORCID 0009-0000-3485-845X
Nobuhiro YamaguchiSchool of Information, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0009-0007-2593-778X
Alberto TodeschiniSchool of Information, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0009-0003-5595-1524
Joseph S ShapiroDepartment of Agricultural and Resource Economics, University of California, Berkeley, Berkeley, CA 94720, USA.ORCID 0009-0002-2410-6355

Funding

Drinking Water Quality, the Health of Older Americans, and InequalityR01AG079914 · NIA · NATIONAL BUREAU OF ECONOMIC RESEARCH · PI Joseph Shapiro · 2023 to 2026
$3.3M
NIA NIH HHS R01 AG079914
6 · The paper itself

Abstract

We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and more than half of wetlands; under a 2020 White House rule, it protects less than half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking-water sources. Our framework can support permitting, policy design, and use of machine learning in regulatory implementation problems.

Indexed as

Drinking WaterMachine LearningRiversWater PollutionWater QualityWetlandsConservation of Natural ResourcesDrinking Water

Identifiers

PMID38271507
PMCPMC11008676

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.