|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|
k nearest neighbours
Feed forward neural network
|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.|
|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|
|Kay,Shinn & Sommerville1999 Parasitology Today.pdf||984.92 kB||Adobe PDF||Under Embargo until 31/12/2999 Request a copy|
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