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For each site (referred to by its SSSI and GCR names) each major and/or significant cave system is inventoried by way of an annotated outline map and appropriate text. Particular attention is paid to the more important and/or extensive groups of calcite speleothems and also the notable sections of undisturbed clastic sediments where known or identified. For both materials, their present state (as of 2001) is noted, but any monitoring exercise will have to start with a more detailed description for reference purposes. Locations of all significant sediments and deposits are indicated on the outline surveys. The scientific value of many caves lies in their passage morphology, as carved into the solid limestone. With rare exceptions, these features are not fragile and are barely impacted from cavers' visits. The main features are identified in this report, but without any attempt or need to detail most of them with reference to any future monitoring. For each site, the overall geological and geomorphological values are outlined only briefly by way of introduction. The reader is referred to Volume 12 of the Geological Conservation Review (Waltham et al., 1997), which contains fully referenced descriptions and evaluations of the geomorphological evolution of the sites. The results of explorations and research since 1997 are summarised in appropriately greater detail. The only references cited are those that post-date 1997 or are not cited in the GCR volume's extensive bibliography. Within nearly all the sites there are many smaller caves and potholes that add collectively to the scientific value of the site. Except for any with special significance, these are not described in detail but are all listed in Table 1 and described in Barrington and Stanton, (1977) and in Limestones and Caves of Wales (Ford, 1989). In particular, many sites are only of archaeological interest, and these are noted in Table 1, but are not dealt with here, as in many cases the entire cave is of interest. Furthermore, many caves contain small patches of undisturbed sediment and odd stalagmite deposits, whose scientific value is hard to quantify. Commonly, a stalagmite deposit may have little aesthetic value (for example if covered in sediment), but its potential scientific value may not be revealed unless a specific study is undertaken. Only some of the caves included in this report have had proper detailed geomorphological surveys, and even these will not have identified or reported all the significant features.

We investigate the power of voting among diverse, randomized software agents. With teams of computer Go agents in mind, we develop a novel theoretical model of two-stage noisy voting that builds on recent work in machine learning. This model allows us to reason about a collection of agents with different biases (determined by the first-stage noise models), which, furthermore, apply randomized algorithms to evaluate alternatives and produce votes (captured by the second-stage noise models). We analytically demonstrate that a uniform team, consisting of multiple instances of any single agent, must make a significant number of mistakes, whereas a diverse team converges to perfection as the number of agents grows. Our experiments, which pit teams of computer Go agents against strong agents, provide evidence for the effectiveness of voting when agents are diverse.

https://viurrspace.ca/bitstream/handle/10613/20448/Jun05-1895.pdf?sequence=2

https://viurrspace.ca/bitstream/handle/10613/22131/Jul26-1879.pdf?sequence=2

https://viurrspace.ca/bitstream/handle/10613/18174/Apr29-1898.pdf?sequence=2

https://viurrspace.ca/bitstream/handle/10613/17011/May18-1900.pdf?sequence=2

https://viurrspace.ca/bitstream/handle/10613/21184/Dec23-1890.pdf?sequence=2&isAllowed=y

Bias correction of climate variables is a standard practice in climate change impact (CCI) studies. Various methodologies have been developed within the framework of quantile mapping. However, it is well known that quantile mapping may significantly modify the long-term statistics due to the time dependency of the temperature bias. Here, a method to overcome this issue without compromising the day-to-day correction statistics is presented. The methodology separates the modeled temperature signal into a normalized and a residual component relative to the modeled reference period climatology, in order to adjust the biases only for the former and preserve the signal of the later. The results show that this method allows for the preservation of the originally modeled long-term signal in the mean, the standard deviation and higher and lower percentiles of temperature. To illustrate the improvements, the methodology is tested on daily time series obtained from five Euro CORDEX regional climate models (RCMs).

https://viurrspace.ca/bitstream/handle/10613/7023/July07-1910.pdf?sequence=2