Mineral Potential of Arctic Canada

Canada is on the threshold of a major new phase in the development of its Arctic resources. The Prudhoe Bay discovery triggered a boom in northern petroleum exploration that is just getting into high gear this year. Mining activity also has greatly expanded in the last few years with some 73 active exploration programs underway and several large new mines just coming into production. The size of these ventures is indicated by ore reserves of two lead-zinc mines to the value of 900 million dollars at Pine Point in southern Northwest Territories, and 1.2 billion dollars at the Anvil property in the southern Yukon Territory. Other properties with very large reserves are currently under development. Approximately 465,000 sq. miles of the 1.5 million square miles of Canada north of the 60th parallel are underlain by sedimentary rocks. A volumetric estimate of petroleum potential on the basis of rather scanty evidence is made at 54 billion barrels. With the current activity in the area it should be possible to improve this estimate considerably in the next 2 or 3 years. The two most promising areas are the Arctic Coastal Plains containing large volumes of young sedimentary rock and large structures, and the Mesozoic Sverdrup Basin also with many potential hydrocarbon traps. The Interior Plains of the mainland and the Arctic lowlands, as well as the fold belts of the Franklinian miogeosyncline indicate lesser potential

Use of computer-aided detection (CAD) tools in screening mammography: a multidisciplinary investigation

We summarise a set of analyses and studies conducted to assess the effects of the use of a computer-aided detection (CAD) tool in breast screening. We have used an interdisciplinary approach that combines: (a) statistical analyses inspired by reliability modelling in engineering; (b) experimental studies of decisions of mammography experts using the tool, interpreted in the light of human factors psychology; and (c) ethnographic observations of the use of the tool both in trial conditions and in everyday screening practice. Our investigations have shown patterns of human behaviour and effects of computer-based advice that would not have been revealed by a standard clinical trial approach. For example, we found that the negligible measured effect of CAD could be explained by a range of effects on experts' decisions, beneficial in some cases and detrimental in others. There is some evidence of the latter effects being due to the experts using the computer tool differently from the intentions of the developers. We integrate insights from the different pieces of evidence and highlight their implications for the design, evaluation and deployment of this sort of computer tool

Towards Detecting Rumours in Social Media

The spread of false rumours during emergencies can jeopardise the well-being of citizens as they are monitoring the stream of news from social media to stay abreast of the latest updates. In this paper, we describe the methodology we have developed within the PHEME project for the collection and sampling of conversational threads, as well as the tool we have developed to facilitate the annotation of these threads so as to identify rumourous ones. We describe the annotation task conducted on threads collected during the 2014 Ferguson unrest and we present and analyse our findings. Our results show that we can collect effectively social media rumours and identify multiple rumours associated with a range of stories that would have been hard to identify by relying on existing techniques that need manual input of rumour-specific keywords

Using Gaussian Processes for Rumour Stance Classification in Social Media

Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a rumourous conversation as either supporting, denying or questioning the rumour. Using a classifier based on Gaussian Processes, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will warn both ordinary users of Twitter and professional news practitioners when a rumour is being rebutted

The elements of a computational infrastructure for social simulation

Applications of simulation modelling in social science domains are varied and increasingly widespread. The effective deployment of simulation models depends on access to diverse datasets, the use of analysis capabilities, the ability to visualize model outcomes and to capture, share and re-use simulations as evidence in research and policy-making. We describe three applications of e-social science that promote social simulation modelling, data management and visualization. An example is outlined in which the three components are brought together in a transport planning context. We discuss opportunities and benefits for the combination of these and other components into an e-infrastructure for social simulation and review recent progress towards the establishment of such an infrastructure