Machine-learned global glacier ice volumes.

Niccolò Maffezzoli, Eric Rignot, Carlo Barbante, Mathieu Morlighem, Troels C Petersen, Sebastiano Vascon

Abstract read

In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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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

6 authors.

Niccolò MaffezzoliDepartment of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, Venezia, 30172, Italy. niccolo.maffezzoli@unive.it.

Eric RignotDepartment of Earth System Science, University of California Irvine, Croul Hall, Irvine, 92627, California, USA.

Carlo BarbanteDepartment of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, Venezia, 30172, Italy.

Mathieu MorlighemDepartment of Earth and Planetary Sciences, Dartmouth College, Hanover, 03755, NH, USA.

Troels C PetersenNiels Bohr Institute, University of Copenhagen, Copenhagen, Denmark.

Sebastiano VasconDepartment of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, Venezia, 30172, Italy.

Funding

HORIZON EUROPE Marie Sklodowska-Curie Actions 101066651

6 · The paper itself

Abstract

We present a global dataset of glacier ice thickness modeled with IceBoost v2.0, a gradient-boosted decision tree scheme trained on 7 million ice thickness measurements and informed by physical and geometrical predictors. We model the distributed ice thickness for every glacier in the two latest Randolph Glacier Inventory releases (RGI v6.0 and v7.0), totaling 215,547 and 274,531 glacier outlines, respectively, plus 955 ice masses contiguous with the Greenland Ice Sheet. IceBoost v2.0 represents the third existing RGI v6.0 global ice volume estimate, and the first for RGI v7.0. On RGI v6.0 we find a global glacier volume of (150 ± 38) × 10

Identifiers

PMID42532983

DOI10.1038/s41597-026-07744-9

PMCPMC13424553

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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.