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Accuracy and consistency of grass pollen identification by human analysts using electron micrographs of surface ornamentation

Mander, Luke; Baker, Sarah J.; Belcher, Claire M.; Haselhorst, Derek S.; Rodriguez, Jacklyn; Thorn, Jessica L.; Tiwari, Shivangi; Urrego, Dunia H.; Wesseln, Cassandra J. and Punyasena, Surangi W. (2014). Accuracy and consistency of grass pollen identification by human analysts using electron micrographs of surface ornamentation. Applications in Plant Sciences, 2(8), article no. 1400031.

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DOI (Digital Object Identifier) Link: https://doi.org/10.3732/apps.1400031
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Abstract

• Premise of the study: Humans frequently identify pollen grains at a taxonomic rank above species. Grass pollen is a classic case of this situation, which has led to the development of computational methods for identifying grass pollen species. This paper aims to provide context for these computational methods by quantifying the accuracy and consistency of human identification.

• Methods: We measured the ability of nine human analysts to identify 12 species of grass pollen using scanning electron microscopy images. These are the same images that were used in computational identifications. We have measured the coverage, accuracy, and consistency of each analyst, and investigated their ability to recognize duplicate images.

• Results: Coverage ranged from 87.5% to 100%. Mean identification accuracy ranged from 46.67% to 87.5%. The identification consistency of each analyst ranged from 32.5% to 87.5%, and each of the nine analysts produced considerably different identification schemes. The proportion of duplicate image pairs that were missed ranged from 6.25% to 58.33%.

• Discussion: The identification errors made by each analyst, which result in a decline in accuracy and consistency, are likely related to psychological factors such as the limited capacity of human memory, fatigue and boredom, recency effects, and positivity bias.

Item Type: Journal Item
Copyright Holders: 2014 The Authors
ISSN: 2168-0450
Project Funding Details:
Funded Project NameProject IDFunding Body
Not SetPIIF-GA-2012-328245EU
Keywords: automation; classification; expert analysis; identification; palynology
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Environment, Earth and Ecosystem Sciences
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 50342
Depositing User: Luke Mander
Date Deposited: 28 Jul 2017 09:21
Last Modified: 13 Oct 2019 08:55
URI: http://oro.open.ac.uk/id/eprint/50342
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