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Reference: Hines, G, Kosmala, M, Swanson, A et al., (2015). Aggregating user input in ecology citizen science projects.Citable link to this page:

 

Aggregating user input in ecology citizen science projects

Abstract: Camera traps (remote, automatic cameras) are revolutionizing large-scale studies in ecology. The Serengeti Lion Project has used camera traps to produce over 1.5 million pictures of animals in the Serengeti. To analyzethese pictures, the Project created Snapshot Serengeti, acitizen science website where volunteers can help classify animals. To increase accuracy, each photo is shownto multiple users and a critical step is aggregating individual classifications. In this paper, we present a newaggregation algorithm which achieves an accuracy of98.6%, better than many human experts. Our algorithmalso requires fewer users per photo than existing methods. The algorithm is intuitive and designed so that nonexperts can understand the end results

Publication status:PublishedPeer Review status:Peer reviewedVersion:Accepted manuscript Funder: Media in Content   Conference Details: Twenty-Seventh Conference on Innovative Applications of Artificial IntelligenceNotes:Copyrightc © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved

Bibliographic Details

Publisher: Association for Computing Machinery

Publisher Website: http://www.acm.org/

Host: Twenty-Seventh Conference on Innovative Applications of Artificial Intelligencesee more from them

Publication Website: http://www.aaai.org/Conferences/AAAI/aaai15.php

Volume: 5

Extent: 3975-3980

Issue Date: 2015-01

pages:3975-3980Identifiers

Urn: uuid:b7b47497-aabe-44d6-8006-2779c2da3f1f

Isbn: 9781577357032

Source identifier: 612642 Item Description

Type: Conference;

Version: Accepted manuscript Tiny URL: pubs:612642

Relationships





Autor: Hines, G - institutionUniversity of Oxford Oxford, MPLS, Physics, Astrophysics - - - Kosmala, M - - - Swanson, A - institutionUni

Fuente: https://ora.ox.ac.uk/objects/uuid:b7b47497-aabe-44d6-8006-2779c2da3f1f



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