Resolving root causes of experiment discrepancies guided by machine learning.

D Neudecker, K J Kelly, S A Vander Wiel, A D Carlson, M J Grosskopf, D A Brown, B Pritychenko, N A W Walton, R C Haight

Abstract read

In one paragraph

Article in Nature communications, 2026. 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.

D NeudeckerLos Alamos National Laboratory, Los Alamos, NM, USA. dneudecker@lanl.gov.ORCID http://orcid.org/0000-0003-3920-0627

K J KellyLos Alamos National Laboratory, Los Alamos, NM, USA.

S A Vander WielLos Alamos National Laboratory, Los Alamos, NM, USA.

A D CarlsonNational Institute of Standards and Technology, Gaithersburg, MD, USA.

M J GrosskopfLos Alamos National Laboratory, Los Alamos, NM, USA.

D A BrownBrookhaven National Laboratory, Upton, NY, USA.ORCID http://orcid.org/0000-0002-9869-0278

B PritychenkoBrookhaven National Laboratory, Upton, NY, USA.ORCID http://orcid.org/0000-0002-3342-8631

N A W WaltonLos Alamos National Laboratory, Los Alamos, NM, USA.

R C HaightLos Alamos National Laboratory, Los Alamos, NM, USA.

Funding

U.S. Department of Energy (DOE) N/A

6 · The paper itself

Abstract

Scientists rely on accurate experimental data to explain nature and then harness this knowledge for applications addressing human needs. However, discrepancies between experiments of the same observable can impede scientific progress if one does not understand the underlying causes. Here, we developed a process that unravels data discrepancies by first using Bayesian machine learning to relate discrepancies to few of many, potentially biasing metadata features that encode experiment procedures. This machine learning output guides human experts to study discrepancy causes by simulating suspicious aspects of historical experiments or designing modern ones to address open questions. The study findings then lead to rejecting or correcting historical data on firm scientific bases. This process is demonstrated for the energy spectrum of neutrons emitted promptly (<1 ns) after fission of

Identifiers

PMID42802175

DOI10.1038/s41467-026-76798-5

PMCPMC13616969

What OpenQuestion holds

Textmetadata

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.