Bernard Michael Bindon - reproductive physiologist, animal scientist, research leader

This paper is a foreword to a series of papers commissioned on 'the impact of science on the beef industry', where the Beef CRC-related collaborative scientific work of Professor Bernard Michael Bindon will be reviewed. These papers will be presented in March 2006, as part of a 'festschrift' to recognise his wider contributions to the Australian livestock industries for over 40 years. Bindon's career involved basic and applied research in many areas of reproductive physiology, genetics, immunology, nutrition, meat science and more recently genomics, in both sheep and cattle. Together with his collaborators, he made large contributions to animal science by improving the knowledge of mechanisms regulating reproductive functions and in elucidating the physiology and genetics of high fecundity livestock. His collaborative studies with many colleagues of the reproductive biology and genetics of the Booroola Merino were amongst the most extensive ever conducted on domestic livestock. He was instrumental in the development of immunological techniques to control ovulation rate and in examining the application of these and other techniques to increase beef cattle reproductive output. This paper tracks his investigations and achievements both within Australia and internationally. In the later stages of his career he was the major influence in attracting a large investment in Cooperative Research Centres for the Australian cattle industry, in which he directed a multi-disciplinary approach to investigate, develop and disseminate science and technology to improve commercial cattle productivity.

Input-driven Language Learning

Input-driven models provide an explicit and readily testable account of language learning. Although we share Ellis's view that the statistical structure of the linguistic environment is a crucial and, until recently, relatively neglected variable in language learning, we also recognize that the approach makes three assumptions about cognition and language learning that are not universally shared. The three assumptions concern (a) the language learner as an intuitive statistician, (b) the constraints on what constitute relevant surface cues, and (c) the redescription problem faced by any system that seeks to derive abstract grammatical relations from the frequency of co-occurring surface forms and functions. These are significant assumptions that must be established if input-driven models are to gain wider acceptance. We comment on these issues and briefly describe a distributed, instance-based approach that retains the key features of the input-driven account advocated by Ellis but that also addresses shortcomings of the current approaches.