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STORRE: Stirling Online Research Repository
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 |
| Author(s): | 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 |
| Date Deposited: | 23-Nov-2012 |
| 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. https://doi.org/10.1016/S0169-4758%2899%2901433-7 |
| 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. |
| DOI Link: | 10.1016/S0169-4758(99)01433-7 |
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| Licence URL(s): | http://www.rioxx.net/licenses/under-embargo-all-rights-reserved |
| File | Description | Size | Format | |
|---|---|---|---|---|
| KayShinn Sommerville1999 Parasitology Today.pdf | Fulltext - Published Version | 984.92 kB | Adobe PDF | Under Embargo until 3000-01-01 Request a copy |
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