Colorado State University

About: Colorado State University is a education organization based out in Fort Collins, Colorado, United States. It is known for research contribution in the topics: Population & Radar. The organization has 31430 authors who have published 69040 publications receiving 2724463 citations. The organization is also known as: CSU & Colorado Agricultural College.

Kullback-Leibler information as a basis for strong inference in ecological studies

Abstract: We describe an information-theoretic paradigm for analysis of ecological data, based on Kullback–Leibler information, that is an extension of likelihood theory and avoids the pitfalls of null hypothesis testing. Information-theoretic approaches emphasise a deliberate focus on the a priori science in developing a set of multiple working hypotheses or models. Simple methods then allow these hypotheses (models) to be ranked from best to worst and scaled to reflect a strength of evidence using the likelihood of each model (gi), given the data and the models in the set (i.e. L(gi | data)). In addition, a variance component due to model-selection uncertainty is included in estimates of precision. There are many cases where formal inference can be based on all the models in the a priori set and this multi-model inference represents a powerful, new approach to valid inference. Finally, we strongly recommend inferences based on a priori considerations be carefully separated from those resulting from some form of data dredging. An example is given for questions related to age- and sex-dependent rates of tag loss in elephant seals (Mirounga leonina).

Stewardship: Choosing Service Over Self-Interest

Abstract: The article reviews the book “Stewardship: Choosing Service Over Self-Interest,” by Peter Block.

A large-scale evaluation of computational protein function prediction

Abstract: Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Measuring Meaningful Work The Work and Meaning Inventory (WAMI)

Abstract: Many people desire work that is meaningful. However, research in this area has attracted diverse ideas about meaningful work (MW), accompanied by an equally disparate collection of ways of assessin...