YOLo 84, No.lc8 JOURNAL OF GEOPHYSICAL RESEARCH
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Transmission Efficiencies of Field - Collected Aphid ( Homoptera : Aphididae ) Vectors of Beet Yellows Virus
J. Econ. Entomol. 84(2). 638-643 (1991) ABSTRACT Alate aphids (Homoptera: Aphididae) were collected at weekly intervals in 1988 in six California sugarbeet fields-three on each side of a boundary line which separates overwintered spring harvest beet fields in Solano County from fall harvested fields in Yolo County. Alate aphids landing on a yellow board during a 45-min morning collection period...
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This report delineates Yolo County Health Department's process to ascertain its optimal methods of participation in syndromic surveillance and health information exchange. As a health department serving a county of just 200,000 residents, Yolo County Health Department needed to operate within strict financial constraints. Meaningful Use legislation enabled it to pursue both syndromic surveillan...
متن کاملInforming Rangeland Stewardship With Research: Lessons Learned From Yolo County, California
Approximately 70 percent of the contiguous United States is in private lands with half of this total in row crop or rangelands. Audubon California’s Landowner Stewardship Program engages with farmers and ranchers on conservation and restoration projects in a manner compatible with existing agricultural operations. To assess the success of these efforts, Audubon, University of California, Davis,...
متن کاملGeostatistical estimation to delineate oxide and sulfide zones using geophysical data; a case study of Chahar Bakhshi vein-type gold deposit, NE Iran
Delineation of oxide and sulfide zones in mineral deposits, especially in gold deposits, is one of the most essential steps in an exploration project that has been traditionally carried out using the drilling results. Since in most mineral exploration projects there is a limited drilling dataset, application of geophysical data can reduce the error in delineation of the sulfide and oxide zones....
متن کاملFast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video
Object detection is considered one of the most challenging problems in this field of computer vision, as it involves the combination of object classification and object localization within a scene. Recently, deep neural networks (DNNs) have been demonstrated to achieve superior object detection performance compared to other approaches, with YOLOv2 (an improved You Only Look Oncemodel) being one...
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