Uncertainty in Coal Property Valuation in West Virginia: A Case Study

Interpolated grids of coal bed thickness are being considered for use in a proposed method for taxation of coal in the state of West Virginia (United States). To assess the origin and magnitude of possible inaccuracies in calculated coal tonnage, we used conditional simulation to generate equiprobable realizations of net coal thickness for two coals on a 7 min topographic quadrangle, and a third coal in a second quadrangle. Coals differed in average thickness and proportion of original coal that had been removed by erosion; all three coals crop out in the study area. Coal tonnage was calculated for each realization and for each interpolated grid for actual and artificial property parcels, and differences were summarized as graphs of percent difference between tonnage calculated from the grid and average tonnage from simulations. Coal in individual parcels was considered minable for valuation purposes if average thickness in each parcel exceeded 30 inches. Results of this study show that over 75% of the parcels are classified correctly as minable or unminable based on interpolation grids of coal bed thickness. Although between 80 and 90% of the tonnages differ by less than 20% between interpolated values and simulated values, a nonlinear conditional bias might exist in estimation of coal tonnage from interpolated thickness, such that tonnage is underestimated where coal is thin, and overestimated where coal is thick. The largest percent differences occur for parcels that are small in area, although because of the small quantities of coal in question, bias is small on an absolute scale for these parcels. For a given parcel size, maximum apparent overestimation of coal tonnage occurs in parcels with an average coal bed thickness near the minable cutoff of 30 in. Conditional bias in tonnage for parcels having a coal thickness exceeding the cutoff by 10 in. or more is constant for two of the three coals studied, and increases slightly with average thickness for the third coal.     

Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and -ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data.     

Binary coefficients: A theoretical and empirical study

Binary coefficients can be assigned to several categories on the basis of algebraic and conceptual properties. The phi coefficient of association is related algebraically to the chi-square statistic for 2-by-2 contingency tables, and use of this coefficient in cluster analysis permits the objective, nonarbitrary partitioning of objects among groups on the basis of previously selected levels of significant, positive association. Similarity, matching, and distance coefficients possess neither conceptual nor operational statistical meaning for many geological data sets. The weighted pair group method and flexible clustering strategy may give an overly conservative partitioning of objects among groups. Clustering by the unweighted pair group method, using the phi coefficient, is recommended for the analysis of biostratigraphic and paleoecologic presence—absence data.     

Emerging fisheries,emerging fishery interactions with sea turtles: A case study of the large-mesh gillnet fishery for flounder in Pamlico Sound,North Carolina,USA

Emerging fisheries and changes in fishery practices are not always readily apparent, nor are their impacts on non-target species such as seabirds, sea turtles, and marine mammals. Data from several different sources led managers to discover high rates of sea turtle bycatch in an inshore large-mesh gillnet fishery in North Carolina, USA, particularly the emerging deep-water gillnet fishery. This paper reviews the history of how increased numbers of observed stranded sea turtles in 1999 led to the discovery that turtles were becoming entangled in the large-mesh gillnet fishery in Pamlico Sound, North Carolina. It also demonstrates how a variety of data sets from fisheries observers, aerial surveys, and fisheries statistics programs contributed to shaping management of the large-mesh gillnet fishery in Pamlico Sound to decrease turtle bycatch and now point towards the need of additional assessment of gillnet bycatch in other parts of North Carolina. Finally, potential approaches are discussed for a more timely detection of future fishery conflicts and development of a plan to reduce otherwise inevitable bycatch and disruptions to fishing effort.