Organic chemical signatures of New Zealand carbonate concretions and calcite fracture fills as potential fluid migration indicators

Macroscopic calcite crystals are common in sedimenta¬ry strata, occurring both as tectonic veins and also filling one or more generations of septarian rupture or later brittle fractures in calcareous concretions. Traces of hydrocarbons are frequently present in calcite crystals, especially near active petroleum systems, and are routinely the object of fluid inclusion studies linking source and migration pathway. Such calcites are shown here also to contain fatty acids in widely varying amounts ranging from 0.2 to more than 5 μg/g. Vein calcites examined are typically near the lower figure, close to analytical blank levels, and this is also true of some concretionary fracture fill calcites, notably those from the Palaeocene Moeraki ‘boulders’. Other concretionary fracture fill calcites (Jurassic, Scotland; Eocene, Waikato Coal Measures and associated marine strata) have much higher fatty acid contents, especially those filling later brittle style fractures. Although usually less abundant than the fatty acids in the concretions themselves, they lack the long chain n-acids derived from terrestrial vegetation and are commonly dominated by dioic acids. Exceptionally, in the calcitic septarian fill of a sideritic Coal Measures concretion, their abundance far exceeds that of concretion body fatty acids. They appear to be fluid transported, probably in aqueous solution, and have molecular signatures potentially distinctive of maturing organic matter sources from which the fluids derived

Optimal land cover mapping and change analysis in northeastern oregon using landsat imagery.

Abstract The necessity for the development of repeatable, efficient, and accurate monitoring of land cover change is paramount to successful management of our planet’s natural resources. This study evaluated a number of remote sensing methods for classifying land cover and land cover change throughout a two-county area in northeastern Oregon (1986 to 2011). In the past three decades, this region has seen significant changes in forest management that have affected land use and land cover. This study employed an accuracy assessment-based empirical approach to test the optimality of a number of advanced digital image processing techniques that have recently emerged in the field of remote sensing. The accuracies are assessed using traditional error matrices, calculated using reference data obtained in the field. We found that, for single-time land cover classification, Bayes pixel-based classification using samples created with scale and shape segmentation parameters of 8 and 0.3, respectively, resulted in the highest overall accuracy. For land cover change detection, using Landsat-5 TM band 7 with a change threshold of 1.75 standard deviations resulted in the highest accuracy for forest harvesting and regeneration mapping

Development of the Motivational Interviewing Supervision and Training Scale

The movement to use empirically supported treatments has increased the need for researchers and supervisors to evaluate therapists’ adherence to and the quality with which they implement those interventions. Few empirically supported approaches exist for providing these types of evaluations. This is also true for motivational interviewing, an empirically supported intervention important in the addictions field. This study describes the development and psychometric evaluation of the Motivational Interviewing Supervision and Training Scale (MISTS), a measure intended for use in training and supervising therapists implementing motivational interviewing. Satisfactory interrater reliability was found (generalizability coefficient p2 = .79), and evidence was found supporting the convergent and discriminant validity of the MISTS. Recommendations for refinement of the measure and future research are discussed

The use of bootstrap methods for analysing health-related quality of life outcomes (particularly the SF-36)

Health-Related Quality of Life (HRQoL) measures are becoming increasingly used in clinical trials as primary outcome measures. Investigators are now asking statisticians for advice on how to analyse studies that have used HRQoL outcomes. HRQoL outcomes, like the SF-36, are usually measured on an ordinal scale. However, most investigators assume that there exists an underlying continuous latent variable that measures HRQoL, and that the actual measured outcomes (the ordered categories), reflect contiguous intervals along this continuum. The ordinal scaling of HRQoL measures means they tend to generate data that have discrete, bounded and skewed distributions. Thus, standard methods of analysis such as the t-test and linear regression that assume Normality and constant variance may not be appropriate. For this reason, conventional statistical advice would suggest that non-parametric methods be used to analyse HRQoL data. The bootstrap is one such computer intensive non-parametric method for analysing data. We used the bootstrap for hypothesis testing and the estimation of standard errors and confidence intervals for parameters, in four datasets (which illustrate the different aspects of study design). We then compared and contrasted the bootstrap with standard methods of analysing HRQoL outcomes. The standard methods included t-tests, linear regression, summary measures and General Linear Models. Overall, in the datasets we studied, using the SF-36 outcome, bootstrap methods produce results similar to conventional statistical methods. This is likely because the t-test and linear regression are robust to the violations of assumptions that HRQoL data are likely to cause (i.e. non-Normality). While particular to our datasets, these findings are likely to generalise to other HRQoL outcomes, which have discrete, bounded and skewed distributions. Future research with other HRQoL outcome measures, interventions and populations, is required to confirm this conclusion