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Asger Roer Pedersen

Researcher at Aarhus University

Publications -  52
Citations -  1455

Asger Roer Pedersen is an academic researcher from Aarhus University. The author has contributed to research in topics: Rehabilitation & Functional Independence Measure. The author has an hindex of 14, co-authored 46 publications receiving 1240 citations.

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Can warm climate‐related structure of littoral predator assemblies weaken the clear water state in shallow lakes?

TL;DR: In this article, the role of aquatic plants in shallow lakes under warm climates was analyzed using artificial submerged and free-floating plant beds in five comparable lakes located in the temperate zone (Denmark, 55-57 °N) and in the subtropical zone (Uruguay, 30-35 °S), with the aim to study the structure and dynamics of the main associated communities.
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Lake Restoration by Fish Removal: Short- and Long-Term Effects in 36 Danish Lakes

TL;DR: In this paper, the authors examined the general and long-term effects obtained after the removal of 41-1360 kg fish ha−1 in 36 mainly shallow and eutrophic lakes.
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Salinity Induced Regime Shift in Shallow Brackish Lagoons

TL;DR: The results have implications for the management of coastal lagoons both at present and in a future (predicted) warmer climate: improved water quality can be obtained by reducing the nutrient loading or enhancing the freshwater input to a level triggering a shift to Daphnia dominance.
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Quantifying walking and standing behaviour of dairy cows using a moving average based on output from an accelerometer.

TL;DR: In this paper, the authors developed algorithms for the detection of walking and standing in dairy cows based on the output from an electronic device quantifying acceleration in three dimensions, and the results suggest that the number of steps taken per time unit as well as the frequency and duration can be estimated with a reasonable accuracy.
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Consistency and asymptotic normality of an approximate maximum likelihood estimator for discretely observed diffusion processes

TL;DR: In this article, an estimator based on an approximation to the (unknown) likelihood function was proposed, with no assumptions on the distance between the discrete observation time-points, and it was shown consistency and asymptotic normality of this estimator.