Evidence map›Paper›PMID 42230895›Full record

ArticleScientific reports2026

Machine-learned dimethyl sulphide (DMS) for the North Atlantic (2002-2024) to support movement studies.

Meixuan Liu, Fernando Benitez-Paez, Oliver Padget, Dmitry Kishkinev, Urška Demšar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. 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

5 authors.

Meixuan LiuSchool of Geography and Sustainable Development, University of St Andrews, St Andrews, UK. ml340@st-andrews.ac.uk.
Fernando Benitez-PaezSchool of Geography and Sustainable Development, University of St Andrews, St Andrews, UK.
Oliver PadgetDepartment of Earth, Ocean and Ecological Sciences, University of Liverpool, Liverpool, UK.
Dmitry KishkinevSchool of Life Sciences, Keele University, Newcastle, UK.
Urška DemšarSchool of Geography and Sustainable Development, University of St Andrews, St Andrews, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dimethyl sulphide (DMS) serves as a key olfactory cue for seabird navigation, yet existing DMS products operate at coarse spatiotemporal resolutions (≥ 25 km, monthly) mismatched to the scales of individual movement decisions. We ask (I) whether machine learning can produce biologically relevant, high-resolution DMS estimates across the North Atlantic, and (ii) whether such estimates can be readily integrated with animal tracking data to support ecological interpretation of animal trajectories. Using North Atlantic in-situ DMS observations (2002-2024) and five satellite-data-based environmental predictors (chlorophyll, mixed layer depth, nitrate, sea-surface temperature, and photosynthetically available radiation), we developed a machine-learning-based ensemble model for DMS prediction that achieved strong accuracy (test R

Indexed as

Animal MigrationBirdsMachine LearningSulfidesAnimalsAtlantic Oceandimethyl sulfideSulfidesBird movementDimethyl sulphideEnsemble methodMachine learningOlfactory navigation

Identifiers

PMID42230895
PMCPMC13470501

What OpenQuestion holds

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Registered trials

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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.