Evidence map›Paper›PMID 40224017›Full record

ArticleiScience2025

Socio-environmental modeling shows physics-like confidence with water modeling surpassing it in numerical claims.

Arnald Puy, Ethan Bacon, Alba Carmona, Samuel Flinders, David Gefen, Mohammad Khanjani, Kai R Larsen, Alessio Lachi, Seth N Linga, Samuele Lo Piano and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 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. Article
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

16 authors.

Arnald PuySchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK.
Ethan BaconSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK.
Alba CarmonaDepartment of Modern Languages, College of Arts and Law, University of Birmingham, Birmingham B15 2TT, UK.
Samuel FlindersSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK.
David GefenLeBow College of Business, Drexel University, Philadelphia, PA 19104, USA.
Mohammad KhanjaniDepartment of Civil Engineering, Sharif University of Technology, Azadi Avenue, Tehran 11155-4313, Iran.
Kai R LarsenOrganizational Leadership and Information Analytics, Leeds School of Business, University of Colorado Boulder, Boulder, CO, USA.
Alessio LachiSaint Camillus International University of Health and Medical Sciences (UniCamillus), Via Sant'Alessandro 8, 00131 Rome, Italy.
Seth N LingaSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK.
Samuele Lo PianoUniversity of Reading, School of the Built Environment, JJ Thompson Building, Whiteknights Campus, Reading RG6 6AF, UK.
Lieke A MelsenHydrology and Environmental Hydraulics Group, Wageningen University, P.O. Box 9101, 6700 HB Wageningen, the Netherlands.
Emily MurraySchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK.
Razi SheikholeslamiDepartment of Civil Engineering, Sharif University of Technology, Azadi Avenue, Tehran 11155-4313, Iran.
Ariana SobhaniSchool of Biosciences, University of Birmingham, Birmingham B15 2TT, UK.
Nanxin WeiSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK.
Andrea SaltelliBarcelona School of Management, Pompeu Fabra University, Carrer de Balmes 132, 08008 Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Several modern scientific fields rely on computationally intensive mathematical models to study uncertain, complex socio-environmental phenomena such as the spread of a virus, climate change, or the water cycle. However, the degree of epistemic commitment of these fields is unclear. By using machine learning to extract the knowledge claims of around 755,000 abstracts from 14 scientific fields spanning the human and physical sciences, we show that epidemic, integrated assessment, and water modeling display a degree of linguistic assertiveness akin to physics. Water modeling surpasses even the most accurate physical sciences in substantiating knowledge claims with numbers, which are largely produced without accompanying uncertainty and sensitivity analysis. By exploring the balance between doubt and certainty in academic writing, our study reflects on whether the strong conviction and quantification of fields modeling socio-environmental processes, especially water modeling, are epistemically justified.

Indexed as

Computational mathematicsEnvironmental scienceInterdisciplinary application studiesWater resources engineering

Identifiers

PMID40224017
PMCPMC11986976

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.