Evidence map›Paper›PMID 35080966›Full record

ArticleScience advances2022

Unexplored Antarctic meteorite collection sites revealed through machine learning.

Veronica Tollenaar, Harry Zekollari, Stef Lhermitte, David M J Tax, Vinciane Debaille, Steven Goderis, Philippe Claeys, Frank Pattyn

Abstract read
In one paragraph

Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

8 authors.

Veronica TollenaarLaboratoire de Glaciologie, Université libre de Bruxelles, Brussels, Belgium.ORCID 0000-0001-9577-1729
Harry ZekollariLaboratoire de Glaciologie, Université libre de Bruxelles, Brussels, Belgium.ORCID 0000-0002-7443-4034
Stef LhermitteDepartment of Geoscience and Remote Sensing, Delft University of Technology, Delft, Netherlands.ORCID 0000-0002-1622-0177
David M J TaxPattern Recognition Laboratory, Delft University of Technology, Delft, Netherlands.ORCID 0000-0002-5153-9087
Vinciane DebailleLaboratoire G-Time, Université libre de Bruxelles, Brussels, Belgium.
Steven GoderisAnalytical, Environmental, and Geo-Chemistry, Vrije Universiteit Brussel, Brussels, Belgium.ORCID 0000-0002-6666-7153
Philippe ClaeysAnalytical, Environmental, and Geo-Chemistry, Vrije Universiteit Brussel, Brussels, Belgium.ORCID 0000-0002-4585-7687
Frank PattynLaboratoire de Glaciologie, Université libre de Bruxelles, Brussels, Belgium.ORCID 0000-0003-4805-5636

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Meteorites provide a unique view into the origin and evolution of the Solar System. Antarctica is the most productive region for recovering meteorites, where these extraterrestrial rocks concentrate at meteorite stranding zones. To date, meteorite-bearing blue ice areas are mostly identified by serendipity and through costly reconnaissance missions. Here, we identify meteorite-rich areas by combining state-of-the-art datasets in a machine learning algorithm and provide continent-wide estimates of the probability to find meteorites at any given location. The resulting set of ca. 600 meteorite stranding zones, with an estimated accuracy of over 80%, reveals the existence of unexplored zones, some of which are located close to research stations. Our analyses suggest that less than 15% of all meteorites at the surface of the Antarctic ice sheet have been recovered to date. The data-driven approach will greatly facilitate the quest to collect the remaining meteorites in a coordinated and cost-effective manner.

Identifiers

PMID35080966
PMCPMC8791461

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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.