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Mark Burgman Rafael Chiaravalloti Fiona Fidler Yizhong Huan Marissa McBride Alexandru Marcoci Juliet Norman Ans Vercammen Bonnie Wintle Yurong Yu 《Conservation Letters》2023,16(1):e12919
Conservation science practitioners seek to preempt irreversible impacts on species, ecosystems, and social–ecological systems, requiring efficient and timely action even when data and understanding are unavailable, incomplete, dated, or biased. These challenges are exacerbated by the scientific community's capacity to consistently distinguish between reliable and unreliable evidence, including the recognition of questionable research practices (QRPs, or “questionable practices”), which may threaten the credibility of research, including harming trust in well-designed and reliable scientific research. In this paper, we propose a “toolkit” for open and pluralistic conservation science, highlighting common questionable practices and sources of bias and indicating where remedies for these problems may be found. The toolkit provides an accessible resource for anyone conducting, reviewing, or using conservation research, to identify sources of false claims or misleading evidence that arise unintentionally, or through misunderstandings or carelessness in the application of scientific methods and analyses. We aim to influence editorial and review practices and hopefully to remedy problems before they are published or deployed in policy or conservation practice. 相似文献
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黄土区大型露天煤矿土地复垦专家系统的程序设计 总被引：4，自引：3，他引：4
介绍了采用智能程序设计语言汉化ＴＵＲＢＯ ＰＲＯＬＧ２．０进行黄土区大型露天煤矿土地复垦专家系统中知识库、推理机、解释机制及知识获取与维护模块的具体程序设计及其实现问题。 相似文献
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Many ecological studies are characterized by paucity of hard data. Statistical analysis in such situations leads to flat‐likelihood functions and wide confidence intervals. Although, there is paucity of hard data, expert knowledge about the phenomenon under study is many times available. Such expert opinion may be used to strengthen statistical inference in these situations. Subjective Bayesian is one approach to incorporate expert opinion in statistical studies. This approach, aside from the subjectivity, also faces operational problems. Elicitation of the prior is the most difficult step. Another is the lack of a precise quantitative definition of what characterizes an expert. In this paper, we discuss a different approach to incorporating subjective expert opinion in statistical analyses. We argue that it is easier to elicit data than to elicit a prior. Such elicited data can then be used to supplement the hard, observed data to possibly improve precision of statistical analyses. The approach suggested here also leads to a natural definition of what constitutes a useful expert. We define a useful expert as one whose opinion adds information over and above what is provided by the observed data. This can be quantified in terms of the change in the Fisher information before and after using the expert opinion. One can, thus, avoid the real possibility of using an expert opinion that adds noise, instead of information, to the hard data. We illustrate this approach using an ecological problem of modeling and predicting occurrence of species. An interesting outcome of this analysis is that statistical thinking helps discriminate between a useful expert and a not so useful expert; expertness need not be decided purely on the basis of experience, fame, or such qualitative characteristics. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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