Evidence map›Paper›PMID 41719386›Full record

ArticleScience advances2026

Machine learning-based paleobathymetric reconstructions using archaeal lipid biomarkers.

Jiaming Zhou, Dujuan Kang, Shijie Chen, Liang Dong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 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

4 authors.

Jiaming ZhouState Key Laboratory of Submarine Geoscience, Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education, Shanghai Key Laboratory of Polar Life and Environment Sciences, and School of Oceanography, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China.ORCID 0009-0001-3896-576X
Dujuan KangState Key Laboratory of Submarine Geoscience, Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education, Shanghai Key Laboratory of Polar Life and Environment Sciences, and School of Oceanography, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China.ORCID 0000-0001-5002-1908
Shijie ChenState Key Laboratory of Submarine Geoscience, Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education, Shanghai Key Laboratory of Polar Life and Environment Sciences, and School of Oceanography, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China.
Liang DongState Key Laboratory of Submarine Geoscience, Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education, Shanghai Key Laboratory of Polar Life and Environment Sciences, and School of Oceanography, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China.ORCID 0000-0001-6118-3697

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate reconstruction of paleo-ocean depths is essential for understanding the interplay between tectonic evolution and global climate change, yet existing methods face substantial limitations. Here, we assess the potential of glycerol dialkyl glycerol tetraethers (GDGTs)-archaeal lipid biomarkers-as a quantitative proxy for paleobathymetry. Analyzing a global dataset of marine surface sediments, we find that GDGT distributions exhibit systematic shifts with water depth. Using random forest machine learning models trained on combined isoGDGT and OH-GDGT profiles, we achieve high predictive performance (

Indexed as

ArchaeaBiomarkersLipidsMachine LearningGeologic SedimentsOceans and SeasRandom ForestBiomarkersLipids

Identifiers

PMID41719386
PMCPMC12922717

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.