Evidence map›Paper›PMID 40596196›Full record

ArticleScientific reports2025

Assessment of spatial autocorrelation and scalability in fine-scale wildfire random forest prediction models.

Madeleine Pascolini-Campbell, Joshua B Fisher, Kerry Cawse-Nicholson, Christine M Lee, Natasha Stavros

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Madeleine Pascolini-CampbellNASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. madeleine.a.pascolini-campbell@jpl.nasa.gov.
Joshua B FisherChapman University, Orange, CA, USA.
Kerry Cawse-NicholsonNASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
Christine M LeeNASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
Natasha StavrosWKID Solutions LLC, Boulder, CO, USA.

Funding

NASA 80NSSC22K0936
6 · The paper itself

Abstract

Wildfire prediction models that can be applied across diverse regions at fine scales (< 100 m) are critical for wildfire management. Remote sensing offers a path forward by providing heterogeneous and dynamic measurements of fuel load, type, and flammability. Machine learning methods such as random forests provide an empirical framework that are high-accuracy, computationally efficient, interpretable and able to model complex ecological relationships. Here we use high resolution (70 m, every 3-5 days) remote sensing observations of evapotranspiration and evaporative stress index, which represent plant water stress, from Ecosystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS), as well as topography and weather data, to predict burn severity and occurrence for 8 large wildfires that burned 3715 km

Identifiers

PMID40596196
PMCPMC12215841

What OpenQuestion holds

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LicenceCC BY
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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.