Evidence map›Paper›PMID 41258138›Full record

ArticleScientific data2025

A Comprehensive Dataset of Factory Farms in California Compiled Using Computer Vision and Human Validation.

Varun Magesh, Nicolas Rothbacher, Saskia Comess, Erin Maneri, Kit Rodolfa, Sara Tartof, Joan Casey, Keeve Nachman, Daniel E Ho

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Varun MageshStanford Regulation, Evaluation and Governance Lab, Stanford Law School, Stanford University, Stanford, CA, USA.
Nicolas RothbacherStanford Regulation, Evaluation and Governance Lab, Stanford Law School, Stanford University, Stanford, CA, USA.
Saskia ComessStanford Regulation, Evaluation and Governance Lab, Stanford Law School, Stanford University, Stanford, CA, USA.ORCID 0000-0003-3388-7659
Erin ManeriStanford Regulation, Evaluation and Governance Lab, Stanford Law School, Stanford University, Stanford, CA, USA.
Kit RodolfaStanford Regulation, Evaluation and Governance Lab, Stanford Law School, Stanford University, Stanford, CA, USA.ORCID 0000-0002-0829-1282
Sara TartofDepartment of Research and Evaluation, Kaiser Permanente of Southern California, Pasadena, CA, USA.
Joan CaseyDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.ORCID 0000-0002-9809-4695
Keeve NachmanDepartment of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Daniel E HoStanford Regulation, Evaluation and Governance Lab, Stanford Law School, Stanford University, Stanford, CA, USA. dho@law.stanford.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Concentrated Animal Feeding Operations (CAFOs) house livestock at high densities for prolonged periods of time, posing substantial risks to environmental and human health. However, limited public information on CAFOs has constrained efforts to quantify their impacts on proximate communities. Gaps in permitting and reporting have severely limited studies that rely primarily on administrative records. We introduce Cal-FF, a near-complete census of CAFOs in California, a large and agriculturally significant state in the United States, with richer facility data than existing administrative data. Cal-FF was constructed using computer vision on satellite imagery, along with extensive human validation. We focus on California, which accounts for about 20% of US livestock production and has been at the forefront of CAFO regulatory innovation. We estimate that our 2,121 facility dataset captures 98% (95% CI [82%, 98%]) of all California CAFOs as of 2017, identifying 222 locations not present in state regulatory records. In addition to improved accuracy, Cal-FF adds a wealth of information about each facility, including validated permit information, land parcel data, satellite imagery, and annotated facility features. These data provide numerous opportunities for research, analysis, and monitoring.

Indexed as

Animal HusbandryFarmsAnimalsCaliforniaHumansLivestockSatellite Imagery

Identifiers

PMID41258138
PMCPMC12630652

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.