Evidence map›Paper›PMID 34505056›Full record

ArticleFrontiers in big data2021

Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere.

Yiyi Huang, Matthäus Kleindessner, Alexey Munishkin, Debvrat Varshney, Pei Guo, Jianwu Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in big data, 2021. 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
2.4field-weighted citation impact, top 12% of its field
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, 26 citations in OpenAlex.

  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

6 authors at 4 institutions in 1 country.

Yiyi HuangDepartment of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, United States.
Matthäus KleindessnerPaul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, United States.
Alexey MunishkinDepartment of Computer Science and Engineering, University of California Santa Cruz, Santa Cruz, CA, United States.
Debvrat VarshneyDepartment of Information Systems, University of Maryland, Baltimore, MD, United States.
Pei GuoDepartment of Information Systems, University of Maryland, Baltimore, MD, United States.
Jianwu WangDepartment of Information Systems, University of Maryland, Baltimore, MD, United States.
University of Maryland, Baltimore · USUniversity of Arizona · USUniversity of California, Santa Cruz · USUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Arctic sea ice has retreated rapidly in the past few decades, which is believed to be driven by various dynamic and thermodynamic processes in the atmosphere. The newly open water resulted from sea ice decline in turn exerts large influence on the atmosphere. Therefore, this study aims to investigate the causality between multiple atmospheric processes and sea ice variations using three distinct data-driven causality approaches that have been proposed recently: Temporal Causality Discovery Framework Non-combinatorial Optimization

Indexed as

arctic sea iceatmosphere-sea ice interactionscausality discoverydirected acyclic graph-graph neural networksnon-combinatorial optimization via trace exponential and augmented lagrangian for structure learningtemporal causality discovery frameworktime series

Identifiers

PMID34505056
PMCPMC8421796
OpenAlexW3195830470

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.