Examining the use of Amazon’s Mechanical Turk for edge extraction of the occlusal surface of fossilized bovid teeth
Gregory J Matthews,
George K Thiruvathukal,
Maxwell P Luetkemeier and
Juliet K Brophy
PLOS ONE, 2017, vol. 12, issue 7, 1-18
Abstract:
In order to reconstruct environments associated with Plio-Pleistocene hominins in southern Africa, researchers frequently rely upon the animals associated with the hominins, in particular, animals in the Family Bovidae. Bovids in southern Africa are typically identified by their teeth. However, identifying the taxon of a bovid tooth is challenging due to various biasing factors. Furthermore, inaccurate identification of fossil bovids can have significant consequences on the reconstructed paleoenvironment. Recent research on the classification of bovid fossil teeth has relied on using elliptical Fourier analysis to summarize the shape of the outline of the occlusal surface of the tooth and the resulting harmonic amplitudes. Currently, an expert in the field must manually place landmarks around the edges of each tooth which is slow and time consuming. This study tests whether it is possible to crowdsource this task, while maintaining the necessary level of quality needed to perform a statistical analysis on each tooth. Amazon Mechanical Turk workers place landmarks on the edge of the tooth which is compared to the performance of an expert in the field. The results suggest that crowdsourcing the digitization process is reliable and replicable. With the technical aspects of digitization managed, researchers can concentrate on analyzing and interpreting the data.
Date: 2017
References: View complete reference list from CitEc
Citations:
Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0179757 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 79757&type=printable (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0179757
DOI: 10.1371/journal.pone.0179757
Access Statistics for this article
More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().