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	Author	Bobrinskiy N.A.
	Title	Mountains of Central Asia			Type	Miscellaneous
	Year	1967	Publication		Abbreviated Journal
	Volume		Issue		Pages	296-321
	Keywords	Middle Asia; mountain; tien shan; Pamir; Hissar ridge; Turkestan ridge; Kopet-Dag ridge; Animals; plants; Issyk-Kul lake; Sary-Chelek; spiders; birds; lizards; marmots; wild sheep; ibex; snow leopard.; 6330; Russian
	Abstract	It provides a zoogeographical description of Central Asia mountains: Tien Shan (west and east), Pamir, the Turkestan and Hissar ridges, and ruinous mountains in Kyzylkum. Distribution of various animal species over the area under study is described. Data concerning Central Asia sheep, ibex, and snow leopard in the alpine meadow zone, and data concerning the otter (in the Tupalang river basin) and grey partridge is presented. The author noted that generally fauna of Tien Shan, Hissar, and Pamir is similar to that of Inner Asia. The other type of fauna more similar to that of Transcaucasia is typical for Kopet-Dag.
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	Notes	Full text available in RussianJournal Title: Fauna and nature of the USSR.			Approved	no
	Call Number	SLN @ rana @ 611			Serial	180
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	Author	Bobrinskiy, N.A.
	Title	Subgenus Leopardus			Type	Miscellaneous
	Year	1935	Publication		Abbreviated Journal
	Volume		Issue		Pages	100-101
	Keywords	Ussr; leopards; snow leopard; distribution; features.; 6300; Russian
	Abstract	Snow Leopard Felis (Leopardus) uncia S c h r † b., 1778 is distributed in the mountains of Central Asia, Turkmenistan (very rare) and Turkestan, on Tarbagatay, Altay, Sayans and in Uriankhay area. Subspecies haven't been described. Body length is about 130 cm, tail length 90 cm.
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	Notes	Full text available in RussianJournal Title: Guide on hunting-game animals of USSR.			Approved	no
	Call Number	SLN @ rana @ 608			Serial	178
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	Author	Bogdanov O.P.
	Title	Snow leopard (Felis uncia)			Type	Miscellaneous
	Year	1961	Publication		Abbreviated Journal
	Volume		Issue		Pages	246-247
	Keywords	Uzbekistan; snow leopard; distriburion; life history; diet; use.; 6360; Russian
	Abstract	In Uzbekistan, this species is distributed in spurs of Tien Shan and Gissar. It preys on ibex, rarer on argalis, roe-deers, young wild boars. In winter, it attacks livestock and sometimes feeds upon marmots and smaller rodents. Snow leopard attacks man very rarely, only when wounded. The economic significance of this species is low, since only few skins are traded. Its dressed skins are used as rugs.
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	Notes	Full text available in RussianJournal Title: Animals of Uzbekistan.			Approved	no
	Call Number	SLN @ rana @ 614			Serial	183
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	Author	Bogdanov O.P.
	Title	The Chatkal state mountain forest biosphere reserve. The Hissar nature reserve			Type	Miscellaneous
	Year	1989	Publication		Abbreviated Journal
	Volume		Issue		Pages	11-28
	Keywords	Uzbekistan; Chatkal nature reserve; Hissar nature reserve; birds; mammals; amphibians; reptiles; plants; rare species; numerous species; snow leopard.; 6370; Russian
	Abstract	In a popular form it describes the origination, nature and fauna of the Chatkal nature reserve. Habitats and ecology of Menzbier's marmot, water-snake, forest dormouse, and fox are described. It also indicates mammal and bird species listed in the Red Book of the USSR black vulture, griffon vulture, bearded vulture, golden eagle, snow leopard, Turkestan lynx, and Tien-Shan brown bear. There are 23 mammal species in the Hissar nature reserve. Ecology of snow leopard and Siberian mountain ibex is described. In the year 1977, 15 Turkestan lynx, about 25 Tien-Shan brown bears, five to seven snow leopards, and 120 150 Siberian mountain ibex were counted in the nature reserve.
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	Notes	Full text available in RussianJournal Title: Nature reserves of Uzbekistan. Stories of naturalist.			Approved	no
	Call Number	SLN @ rana @ 615			Serial	184
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	Author	Bogdanov O.P.
	Title	Snow leopard or irbis Uncia Uncia			Type	Miscellaneous
	Year	1992	Publication		Abbreviated Journal
	Volume		Issue		Pages	76-80
	Keywords	Ussr; Uzbekistan; Pamir; tien shan; Hissar ridge; Turkestan ridge; Chatkal ridge; juniper forest; snow leopard; species range; hunting; behavior; diet; reproductive activity; number; ibex; mountain sheep.; 6380; Russian
	Abstract	Snow leopard and its habitat within the USSR and Uzbek SSR are described. Its habitat in the Chatkal and Hissar ridges are described too. Given are data concerning alimentary biology, reproduction, and attitude to man. Female snow leopards become mature at the age of two three years, male at the age of four years. Reproduction occurs once every two years. Presumably, there are 10 animals in the country. Snow leopard is protected in four nature reserves in Uzbekistan and a number of nature reserves in neighbour countries.
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	Notes	Full text available in RussianJournal Title: Rare and endangered animals of Uzbekistan. Encyclopedic reference book.			Approved	no
	Call Number	SLN @ rana @ 616			Serial	185
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	Author	Bohnett, E., Faryabi, S. P., Lewison, R., An, L., Bian, X., Rajabi, A. M., Jahed, N., Rooyesh, H., Mills, E., Ramos, S., Mesnildrey, N., Perez, C. M. S., Taylor, J., Terentyev, V., Ostrowski, S.
	Title	Human expertise combined with artificial intelligence improves performance of snow leopard camera trap studies			Type	Journal Article
	Year	2023	Publication	Global Ecology & Conservation	Abbreviated Journal
	Volume	41	Issue	e02350	Pages	1-13
	Keywords	Snow leopard, Artificial intelligence, Camera trap misclassification, individual ID, HotSpotter
	Abstract	Camera trapping is the most widely used data collection method for estimating snow leopard (Panthera uncia) abundance; however, the accuracy of this method is limited by human observer errors from misclassifying individuals in camera trap images. We evaluated the extent Whiskerbook (www.whiskerbook.org), an artificial intelligence (AI) software, could reduce this error rate and enhance the accuracy of capture-recapture abundance estimates. Using 439 images of 34 captive snow leopard individuals, classification was performed by five observers with prior experience in individual snow leopard ID (“experts”) and five observers with no such experience (“novices”). The “expert” observers classified 35 out of 34 snow leopard individuals, on average erroneously splitting one individual into two, thus resulting in a higher number than true individuals. The success rate of experts was 90 %, with less than a 3 % error in estimating the population size in capture-recapture modeling. However, the “novice” observers successfully matched 71 % of encounters, recognizing 25 out of 34 individuals, underestimating the population by 25 %. It was found that expert observers significantly outperformed novice observers, making statistically fewer errors (Mann Whitney U test P = 0.01) and finding the true number of individuals (P = 0.01). These differences were contrasted with a previous study by Johansson et al. 2020, using the same subset of 16 individuals from European zoos. With the help of AI and the Whiskerbook platform, “experts” were able to match 87 % of encounters and identify 15 out of 16 individuals, with modeled estimates of 16 ± 1 individuals. In contrast, “novices” were 63 % accurate in matching encounters and identified 12 out of 16 individuals, modeling 12 ± 1 individuals that underestimated the population size by 12 %. When comparing the performance of observers using AI and the Whiskerbook platform to observers performing the tasks manually, we found that observers using Whiskerbook made significantly fewer errors in splitting one individual into two (P = 0.04). However, there were also a significantly higher number of combination errors, where two individuals were combined into one (P = 0.01). Specifically, combination errors were found to be made by “novices” (P = 0.04). Although AI benefited both expert and novice observers, expert observers outperformed novices. Our results suggest that AI effectively reduced the misclassification of individual snow leopards in camera trap studies, improving abundance estimates. However, even with AI support, expert observers were needed to obtain the most accurate estimates.
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	Notes				Approved	no
	Call Number	SLN @ rakhee @			Serial	1715
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	Author	Bohnett, E., Holmberg, J., Faryabi, S. P., An, L., Ahmad, B., Rashid, W., Ostrowski, S.
	Title	Comparison of two individual identification algorithms for snow leopards (Panthera uncia) after automated detection			Type	Journal Article
	Year	2023	Publication	Ecological Informatics	Abbreviated Journal
	Volume	77	Issue	102214	Pages	1-14
	Keywords	Background subtraction, Deep learning, Hotspotter, Individual identification, PIE v2, Snow leopards
	Abstract	Photo-identification of individual snow leopards (Panthera uncia) is the primary data source for density estimation via capture-recapture statistical methods. To identify individual snow leopards in camera trap imagery, it is necessary to match individuals from a large number of images from multiple cameras and historical catalogues, which is both time-consuming and costly. The camouflaged snow leopards also make it difficult for machine learning to classify photos, as they blend in so well with the surrounding mountain environment, rendering applicable software solutions unavailable for the species. To potentially make snow leopard individual identification available via an artificial intelligence (AI) software interface, we first trained and evaluated image classification techniques for a convolutional neural network, pose invariant embeddings (PIE) (a triplet loss network), and compared the accuracy of PIE to that of the HotSpotter algorithm (a SIFT-based algorithm). Data were acquired from a curated library of free-ranging snow leopards taken in Afghanistan between 2012 and 2019 and from captive animals in zoos in Finland, Sweden, Germany, and the United States. We discovered several flaws in the initial PIE model, such as a small amount of background matching, that was addressed, albeit likely not fixed, using background subtraction (BGS) and left-right mirroring (LR) techniques which demonstrated reasonable accuracy (Rank 1: 74% Rank-5: 92%) comparable to the Hotspotter results (Rank 1: 74% Rank 2: 84%)The PIE BGS LR model, in conjunction with Hotspotter, yielded the following results: Rank-1: 85%, Rank-5: 95%, Rank-20: 99%. In general, our findings indicate that PIE BGS LR, in conjunction with HotSpotter, can classify snow leopards more accurately than using either algorithm alone.
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	Notes				Approved	no
	Call Number	SLN @ rakhee @			Serial	1723
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	Author	Bower, J.N.
	Title	For some endangered species, captive breeding programs are their last chance for life			Type	Magazine Article
	Year	1980	Publication	National Parks and Conservation Magazine	Abbreviated Journal
	Volume		Issue	June	Pages	16-19
	Keywords	snow leopard, captive breeding
	Abstract
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	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language		Original Title
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	Notes				Approved	no
	Call Number	SLN @ rana @			Serial	1209
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	Author	Bower, J.N.
	Title	Shy, elusive, struggling to survive: the snow leopard			Type	Journal Article
	Year	1983	Publication	The Explorer	Abbreviated Journal
	Volume		Issue		Pages	9-11
	Keywords	snow leopard
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
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	Notes				Approved	no
	Call Number	SLN @ rana @ 985			Serial	187
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	Author	Bowling, B.
	Title	The Legal Status of Snow Leopards in Afghanistan			Type	Miscellaneous
	Year	2004	Publication		Abbreviated Journal
	Volume		Issue		Pages
	Keywords	legal; snow leopard; Afghanistan; Cities; Cbd; Convention on Biological Diversity; 5740
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	Publisher	United Nations Environment Programme	Place of Publication		Editor
	Language		Summary Language		Original Title
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	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	SLN @ rana @ 502			Serial	188
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