|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|
Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependant on the depositor still being contactable at their original email address.
This item is protected by original copyright
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
If you believe that any material held in STORRE infringes copyright, please contact email@example.com providing details and we will remove the Work from public display in STORRE and investigate your claim.