A review of the benefits and drawbacks to virtual field guides in today’s Geoscience higher education environment

Virtual Field Guides are a way for educators to tackle the growing issue of funding pressures in areas of higher education, such as geography. Virtual Field Guides are however underutilised and can offer students a different way of learning. Virtual Field Guides have many benefits to students, such as being more inclusive, building student skills and confidence in a controlled environment pre fieldtrip and can increase engagement in the topic studied. There are also benefits to the educator, such as reduced cost, more efficient students on fieldwork tasks and the ability to tailor and update their field guides to suit their needs. However there are drawbacks in the challenge of creation and their outcome as educational standalone tools. This paper reviews the literature around the benefits and draw backs to the creation and incorporation of virtual field guides in geoscience education. © 2017, The Author(s)

Statistics for Fission-Track Thermochronology

This chapter introduces statistical tools to extract geologically meaningful information from fission-track (FT) data using both the external detector and LA-ICP-MS methods. The spontaneous fission of 238U is a Poisson process resulting in large single-grain age uncertainties. To overcome this imprecision, it is nearly always necessary to analyse multiple grains per sample. The degree to which the analytical uncertainties can explain the observed scatter of the single-grain data can be visually assessed on a radial plot and objectively quantified by a chi-square test. For sufficiently low values of the chi-square statistic (or sufficiently high p values), the pooled age of all the grains gives a suitable description of the underlying ‘true’ age population. Samples may fail the chi-square test for several reasons. A first possibility is that the true age population does not consist of a single discrete age component, but is characterised by a continuous range of ages. In this case, a ‘random effects’ model can constrain the true age distribution using two parameters: the ‘central age’ and the ‘(over)dispersion’. A second reason why FT data sets might fail the chi-square test is if they are underlain by multimodal age distributions. Such distributions may consist of discrete age components, continuous age distributions, or a combination of the two. Formalised statistical tests such as chi-square can be useful in preventing overfitting of relatively small data sets. However, they should be used with caution when applied to large data sets (including length measurements) which generate sufficient statistical ‘power’ to reject any simple yet geologically plausible hypothesis