Institution
Oregon State University
Education•Corvallis, Oregon, United States•
About: Oregon State University is a education organization based out in Corvallis, Oregon, United States. It is known for research contribution in the topics: Population & Gene. The organization has 28192 authors who have published 64044 publications receiving 2634108 citations. The organization is also known as: Oregon Agricultural College & OSU.
Topics: Population, Gene, Context (language use), Climate change, Soil water
Papers published on a yearly basis
Papers
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TL;DR: A workflow to evaluate cloud and cloud shadow masking algorithms using cloud validation masks manually derived from both Landsat 7 Enhanced Thematic Mapper Plus and Landsat 8 OLI/TIRS data is created, finding that CFMask, C code based on the Function of Mask (Fmask) algorithm, and its confidence bands have the best overall accuracy among the many algorithms tested using validation data.
648 citations
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TL;DR: Results indicate that climate change could alter stages and rates of development of the pathogen, modify host resistance, and result in changes in the physiology of host-pathogen interactions.
Abstract: ▪ Abstract Research on impacts of climate change on plant diseases has been limited, with most work concentrating on the effects of a single atmospheric constituent or meteorological variable on the host, pathogen, or the interaction of the two under controlled conditions. Results indicate that climate change could alter stages and rates of development of the pathogen, modify host resistance, and result in changes in the physiology of host-pathogen interactions. The most likely consequences are shifts in the geographical distribution of host and pathogen and altered crop losses, caused in part by changes in the efficacy of control strategies. Recent developments in experimental and modeling techniques offer considerable promise for developing an improved capability for climate change impact assessment and mitigation. Compared with major technological, environmental, and socioeconomic changes affecting agricultural production during the next century, climate change may be less important; it will, however, ...
644 citations
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TL;DR: In this article, a study of water isotopes in an Oregon watershed suggests that trees and streams tap into separate water reservoirs instead of using translatory flow, which assumes that water at any soil depth is well mixed.
Abstract: Water movement in upland humid watersheds from the soil surface to the stream is often described using the concept of translatory flow, which assumes that water at any soil depth is well mixed. A study of water isotopes in an Oregon watershed instead suggests that trees and streams tap into separate water reservoirs.
644 citations
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TL;DR: In this article, models from atmospheric science, plant science, and agricultural economics are linked to explore the sensitivity of agricultural productivity to global climate change, and the simulation suggests that irrigated acreage will expand and regional patterns of U.S. agriculture will shift.
Abstract: Agricultural productivity is expected to be sensitive to global climate change. Models from atmospheric science, plant science, and agricultural economics are linked to explore this sensitivity. Although the results depend on the severity of climate change and the compensating effects of carbon dioxide on crop yields, the simulation suggests that irrigated acreage will expand and regional patterns of U.S. agriculture will shift. The impact of the U.S. economy strongly depends on which climate model is used.
644 citations
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07 Dec 2015TL;DR: It is demonstrated that a classical MHT implementation from the 90's can come surprisingly close to the performance of state-of-the-art methods on standard benchmark datasets, and it is shown that appearance models can be learned efficiently via a regularized least squares framework.
Abstract: This paper revisits the classical multiple hypotheses tracking (MHT) algorithm in a tracking-by-detection framework. The success of MHT largely depends on the ability to maintain a small list of potential hypotheses, which can be facilitated with the accurate object detectors that are currently available. We demonstrate that a classical MHT implementation from the 90's can come surprisingly close to the performance of state-of-the-art methods on standard benchmark datasets. In order to further utilize the strength of MHT in exploiting higher-order information, we introduce a method for training online appearance models for each track hypothesis. We show that appearance models can be learned efficiently via a regularized least squares framework, requiring only a few extra operations for each hypothesis branch. We obtain state-of-the-art results on popular tracking-by-detection datasets such as PETS and the recent MOT challenge.
642 citations
Authors
Showing all 28447 results
Name | H-index | Papers | Citations |
---|---|---|---|
Robert Stone | 160 | 1756 | 167901 |
Menachem Elimelech | 157 | 547 | 95285 |
Thomas J. Smith | 140 | 1775 | 113919 |
Harold A. Mooney | 135 | 450 | 100404 |
Jerry M. Melillo | 134 | 383 | 68894 |
John F. Thompson | 132 | 1420 | 95894 |
Thomas N. Williams | 132 | 1145 | 95109 |
Peter M. Vitousek | 127 | 352 | 96184 |
Steven W. Running | 126 | 355 | 76265 |
Vincenzo Di Marzo | 126 | 659 | 60240 |
J. D. Hansen | 122 | 975 | 76198 |
Peter Molnar | 118 | 446 | 53480 |
Michael R. Hoffmann | 109 | 500 | 63474 |
David Pollard | 108 | 438 | 39550 |
David J. Hill | 107 | 1364 | 57746 |