Evaluating Learning Tasks Commonly Applied in Detection Dog Training

2009 ◽  
pp. 99-114 ◽  
Author(s):  
Lisa Lit
PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243122
Author(s):  
Dominique Grandjean ◽  
Riad Sarkis ◽  
Clothilde Lecoq-Julien ◽  
Aymeric Benard ◽  
Vinciane Roger ◽  
...  

The aim of this proof-of-concept study was to evaluate if trained dogs could discriminate between sweat samples from symptomatic COVID-19 positive individuals (SARS-CoV-2 PCR positive) and those from asymptomatic COVID-19 negative individuals. The study was conducted at 2 sites (Paris, France, and Beirut, Lebanon), followed the same training and testing protocols, and involved six detection dogs (three explosive detection dogs, one search and rescue dog, and two colon cancer detection dogs). A total of 177 individuals were recruited for the study (95 symptomatic COVID-19 positive and 82 asymptomatic COVID-19 negative individuals) from five hospitals, and one underarm sweat sample per individual was collected. The dog training sessions lasted between one and three weeks. Once trained, the dog had to mark the COVID-19 positive sample randomly placed behind one of three or four olfactory cones (the other cones contained at least one COVID-19 negative sample and between zero and two mocks). During the testing session, a COVID-19 positive sample could be used up to a maximum of three times for one dog. The dog and its handler were both blinded to the COVID-positive sample location. The success rate per dog (i.e., the number of correct indications divided by the number of trials) ranged from 76% to 100%. The lower bound of the 95% confidence interval of the estimated success rate was most of the time higher than the success rate obtained by chance after removing the number of mocks from calculations. These results provide some evidence that detection dogs may be able to discriminate between sweat samples from symptomatic COVID-19 individuals and those from asymptomatic COVID-19 negative individuals. However, due to the limitations of this proof-of-concept study (including using some COVID-19 samples more than once and potential confounding biases), these results must be confirmed in validation studies.


Animals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 2204
Author(s):  
Wei-Lien Chi ◽  
Ching-Hui Chen ◽  
Hui-Min Lin ◽  
Chung-Chi Lin ◽  
Wang-Ting Chen ◽  
...  

The red imported fire ant (RIFA, Solenopsis invicta) is an exotic aggressive pest that is notorious for its ability to seriously harm humans and animals, cause economic loss to agriculture, and damage ecosystems. This is the first study to validate the capability of filter paper adsorption as a feasible odor bearer of RIFAs and evaluate its use in detection dog training. Two live RIFA-experienced detection dogs achieved a mean 92% positive indication rate (PIR) on RIFA-scented papers with a relatively low false response rate (0.8%). The similar accuracies in recognizing live RIFAs (96%) and scented papers (92%) suggest that a filter paper is an effective odor reservoir. After training with live RIFA and scented filter papers, both RIFA-experienced and inexperienced detection dogs successfully indicated filter papers that were scented with at least 10 RIFAs for 4 h with a high PIR (>93%) and low false response rate (2%). Detection dogs correctly recognized the filter papers scented by 10 RIFAs for 24 h with a 97.6% PIR. Even for scented samples stored at −20 °C and 4 °C for 13 weeks, the positive indication rates (PIRs) were as high as 90%. These results suggest that filter paper is an effective RIFA odor bearer, and the scent can be maintained at least 13 weeks for dog identification. After RIFA-scented paper training, detection dogs showed high (>95%) PIRs for both RIFA-scented paper and live RIFAs and also successfully performed field studies. Using filter paper as a RIFA odor bearer is an effective and economical method for detection dog training and RIFA identification.


2013 ◽  
Author(s):  
Joseph Boomer ◽  
Alexandria C. Zakrzewski ◽  
Jennifer R. Johnston ◽  
Barbara A. Church ◽  
Robert Musgrave ◽  
...  

2009 ◽  
Author(s):  
Jennifer Alvarez ◽  
Rick Yount ◽  
Melissa Puckett ◽  
Caroline Wyman ◽  
Caitlin McLean ◽  
...  

1967 ◽  
Author(s):  
Harold D. Fishbein ◽  
James Benton ◽  
Marta Osborne ◽  
Hirsch Wise

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