A Methodology for Evaluating Operator Usage of Machine Learning Recommendations for Power Grid Contingency Analysis.

John Wenskovitch, Brett Jefferson, Alexander Anderson, Jessica Baweja, Danielle Ciesielski, Corey Fallon

Abstract read

In one paragraph

Article in Frontiers in big data, 2022. 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

6 authors.

John WenskovitchPacific Northwest National Laboratory, National Security Directorate, Richland, WA, United States.

Brett JeffersonPacific Northwest National Laboratory, National Security Directorate, Richland, WA, United States.

Alexander AndersonPacific Northwest National Laboratory, Energy and Environment Directorate, Richland, WA, United States.

Jessica BawejaPacific Northwest National Laboratory, National Security Directorate, Richland, WA, United States.

Danielle CiesielskiPacific Northwest National Laboratory, National Security Directorate, Richland, WA, United States.

Corey FallonPacific Northwest National Laboratory, National Security Directorate, Richland, WA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work presents the application of a methodology to measure domain expert trust and workload, elicit feedback, and understand the technological usability and impact when a machine learning assistant is introduced into contingency analysis for real-time power grid simulation. The goal of this framework is to rapidly collect and analyze a broad variety of human factors data in order to accelerate the development and evaluation loop for deploying machine learning applications. We describe our methodology and analysis, and we discuss insights gained from a pilot participant about the current usability state of an early technology readiness level (TRL) artificial neural network (ANN) recommender.

Indexed as

cognitive loadcontingency analysishuman-machine teamingpower gridtrust evaluation

Identifiers

PMID35774852

DOI10.3389/fdata.2022.897295

PMCPMC9237339

What OpenQuestion holds

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LicenceCC BY

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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.