Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/10104
Appears in Collections:Aquaculture Journal Articles
Peer Review Status: Refereed
Title: Towards an automated system for the identification of notifiable pathogens: Using Gyrodactylus salaris as an example
Authors: Kay, James W
Shinn, Andrew
Sommerville, Christina
Contact Email: aps1@stir.ac.uk
Keywords: Parasitology
Pharmacology
Diagnostics
Statistical classifiers
Gyrodactylus
Monogenea
Notifiable pathogen
k nearest neighbours
Feed forward neural network
Issue Date: May-1999
Publisher: Elsevier
Citation: Kay JW, Shinn A & Sommerville C (1999) Towards an automated system for the identification of notifiable pathogens: Using Gyrodactylus salaris as an example, Parasitology Today, 15 (5), pp. 201-206.
Abstract: Simple and rapid identification of pathogen species is crucial to the control of many diseases. Here, James Kay, Andrew Shinn and Christina Sommerville demonstrate that statistical classifiers discriminate a notifiable pathogen Gyrodactylus salaris Malmberg, 1957, a lethal ectoparasite of Atlantic salmon, Salmo salar L., from its benign close relatives.
Type: Journal Article
URI: http://hdl.handle.net/1893/10104
DOI Link: http://dx.doi.org/10.1016/S0169-4758(99)01433-7
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: University of Glasgow
Aquaculture
Aquaculture

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