Journal•ISSN: 0303-6758
Journal of The Royal Society of New Zealand
Royal Society Te Apārangi
About: Journal of The Royal Society of New Zealand is an academic journal published by Royal Society Te Apārangi. The journal publishes majorly in the area(s): Aotearoa & Genus. It has an ISSN identifier of 0303-6758. Over the lifetime, 1448 publications have been published receiving 26380 citations. The journal is also known as: RSNZ/Journal of the RSNZ.
Topics: Aotearoa, Genus, Population, Medicine, Biology
Papers published on a yearly basis
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TL;DR: The authors describe an ongoing study of a large group of red deer (Cervus elaphus) on Rhum, an island off the west coast of Scotland, in the UK.
Abstract: This fascinating book describes an ongoing study of a large group of red deer (Cervus elaphus) on Rhum, an island off the west coast of Scotland.
506 citations
TL;DR: In this paper, the authors discuss the relationship between Indigenous ways of knowing and the study of environmental change, and present a survey of the work of the Royal Society of New Zealand.
Abstract: (2009). Indigenous ways of knowing and the study of environmental change. Journal of the Royal Society of New Zealand: Vol. 39, No. 4, pp. 151-156.
375 citations
TL;DR: This excellent little book concentrates on theory, especially aspects of viability analysis, although there is a case study, and also examples of interagency activities.
Abstract: How many individuals are enough to ensure long term survival of a species? What are the effects of chance on demographic structure and genetic variability? How can we work together to ensure viable populations? These questions and others are explored in this excellent little book. It concentrates on theory, especially aspects of viability analysis, although there is a case study, and also examples of interagency activities. As in any book, readers can find passages that support their pre-existing views, but taken as a whole this one provides considerable balance. The editor admits that in places he is arguing for his personal preference. Other authors stress that many decisions in conservation biology rightly belong to the community and not just the scientists for whom this book is primarily intended.
267 citations
TL;DR: This paper provides a review on evolutionary machine learning techniques for major machine learning tasks such as classification, regression and clustering, and emerging topics including combinatorial optimisation, computer vision, deep learning, transfer learning, and ensemble learning.
Abstract: Artificial intelligence (AI) emphasises the creation of intelligent machines/systems that function like humans. AI has been applied to many real-world applications. Machine learning is a branch of ...
160 citations
155 citations