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使用多种结构相关气味剂进行训练无法提高家养犬对硝酸铵检测的泛化能力()。

Training with Multiple Structurally Related Odorants Fails to Improve Generalization of Ammonium Nitrate Detection in Domesticated Dogs ().

作者信息

Dorman David C, Foster Melanie L, Lazarowski Lucia

机构信息

Department of Molecular and Biomedical Sciences, North Carolina State University, College of Veterinary Medicine, Raleigh, NC 27607, USA.

出版信息

Animals (Basel). 2021 Jan 16;11(1):213. doi: 10.3390/ani11010213.

Abstract

A critical aspect of canine scent detection involves the animal's ability to respond to odors based on prior odor training. In the current study, dogs ( = 12) were initially trained on an olfactory simple discrimination task using vanillin as the target odorant. Based on their performance on this task, dogs were assigned to experimental groups. Dogs in group 1 and 2 ( = 5 dogs/group; 1 dog/group were removed due to low motivation or high error rates) were trained with either two or six forms of ammonium nitrate (AN), respectively. Dogs were then assessed with a mock explosive with AN and powdered aluminum. Dogs in both groups failed to respond to the novel AN-aluminum odor. Mean success rates were 56 ± 5 and 54 ± 4% for groups 1 and 2, respectively. Overall, and individual dog performance was not statistically higher than chance indicating that dogs did not generalize from AN to a similar AN-based odorant at reliable levels desired for explosive detection dogs. These results suggest the use of authentic explosive materials, without the added complication of including category-learning methods, likely remains a cost-effective and efficient way to train explosive scent detection dogs.

摘要

犬类嗅觉检测的一个关键方面涉及动物根据先前的气味训练对气味做出反应的能力。在当前的研究中,12只狗最初接受了使用香草醛作为目标气味剂的嗅觉简单辨别任务训练。根据它们在这项任务中的表现,将狗分配到实验组。第1组和第2组的狗(每组5只狗;每组各有1只狗因积极性低或错误率高而被剔除)分别接受了两种或六种硝酸铵(AN)形式的训练。然后用含有硝酸铵和铝粉的模拟爆炸物对狗进行评估。两组的狗都未能对新的硝酸铵 - 铝气味做出反应。第1组和第2组的平均成功率分别为56±5%和54±4%。总体而言,个体狗的表现没有统计学意义上高于随机水平,这表明狗没有按照爆炸物检测犬所需的可靠水平从硝酸铵推广到类似的基于硝酸铵的气味剂。这些结果表明,使用真实的爆炸物材料,而不增加类别学习方法的复杂性,可能仍然是训练爆炸物气味检测犬的一种经济有效且高效的方法。

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本文引用的文献

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