Helsen, Gilis, and Weston (2006) do not err in questioning the optical error hypothesis as the only major account for explaining offside decision-making errors     

Helsen W  Gilis B  Weston M 《Journal of sports sciences》2007,25(9):991-994

Oudejans, Bakker, and Beek (2007) recognize several relevant aspects of offside judgements in association football in the paper by Helsen, Gilis, and Weston (2006). We agree that the existing knowledge base on offside assessment needs to be expanded for two reasons. First, from a theoretical point of view it is important to examine how assistant referees can learn to deal with the limitations of the human visual information processing system. Second, from a practical point of view it is relevant to understand better refereeing performances and to identify potential explanations for incorrect offside decisions that could impact on the final outcome of the game. Oudejans et al. (2007) believe we both misinterpreted the optical error hypothesis and that our data set was unsuited to test it. Below, we react to these comments.  相似文献   

Clinical librarians: a journey through a clinical question     

Rigby E  Reid L  Schipperheijn JA  Weston L  Ikkos G 《Health information and libraries journal》2002,19(3):158-160

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The politics of informed consent     

Weston HB 《National forum》1978,58(4):23-26

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Knowing What the Patron Wants: Using Predictive Analytics to Transform Library Decision Making     

Ryan Litsey  Weston Mauldin 《The Journal of Academic Librarianship》2018,44(1):140-144

Predictive analytics and machine learning are burgeoning areas of professional practice for large corporations especially businesses that offer products and services to customers. The power to better understand the movement of large amounts of data in a company and the capability to deploy that data to meet a customer's needs is invaluable from a services standpoint. Some in libraries have theorized that this type of data usage could possibly be used in a library service environment as well. In this article, we demonstrate how you can develop and use machine learning algorithms and predictive analytics to proactively understand library behavior. Although libraries are good at data collection, we often rely on statics or old data for assessment. Utilizing a machine learning system, called the Automated Library Information Exchange Network (ALIEN), we can better understand the movement of the items in the collection and better serve the needs of our customers the library patrons.  相似文献