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Dense Sampling (dense + sampling)
Selected AbstractsVariability of dry sediment bulk density between and within retention ponds and its impact on the calculation of sediment yieldsEARTH SURFACE PROCESSES AND LANDFORMS, Issue 4 2001Gert Verstraeten Abstract Monitoring sediment yields from catchments is important for assessing overall denudation rates and the impact of environmental change. One of the methods used to assess sediment yield is by quantifying sedimentation rates in reservoirs, lakes or small ponds. Before reliable sediment yield values (t ha,1 a,1) can be computed from such sedimentation records, the measured sediment volumes need to be converted to sediment masses using representative values of the dry sediment bulk density. In textbooks, simple relations predicting dry sediment bulk density from sediment texture, time since deposition and hydrologic condition are presented. In this study, 13 small flood retention ponds in central Belgium were sampled to reveal the variability in dry sediment bulk density and to test the commonly used relations to predict dry sediment bulk density. Dry sediment bulk density varies not only between the selected ponds (0·78,1·35 t,m,3) but also within individual ponds (coefficient of variation at 95 per cent ranges from 7 to 80 per cent). The observed variability can be attributed primarily to the hydrologic condition of the retention pond and, also, to sediment texture. The existing relations are not a reliable predictor for the observed dry bulk densities, because they are primarily based on sediment texture. Thus, when using volumetric sedimentation data from small ponds with varying hydrologic condition to predict sediment yield, existing relations predicting dry sediment bulk density cannot be applied. Instead, frequent and dense sampling of sediments is necessary to calculate a representative value of the dry sediment bulk density. Copyright © 2001 John Wiley & Sons, Ltd. [source] Spatial point-process statistics: concepts and application to the analysis of lead contamination in urban soil,ENVIRONMETRICS, Issue 4 2005Christian Walter Abstract This article explores the use of spatial point-process analysis as an aid to describe topsoil lead distribution in urban environments. The data used were collected in Glebe, an inner suburb of Sydney. The approach focuses on the locations of punctual events defining a point pattern, which can be statistically described through local intensity estimates and between-point distance functions. F -, G - and K -surfaces of a marked spatial point pattern were described and used to estimate nearest distance functions over a sliding band of quantiles belonging to the marking variable. This provided a continuous view of the point pattern properties as a function of the marking variable. Several random fields were simulated by selecting points from random, clustered or regular point processes and diffusing them. Recognition of the underlying point process using variograms derived from dense sampling was difficult because, structurally, the variograms were very similar. Point-event distance functions were useful complimentary tools that, in most cases, enabled clear recognition of the clustered processes. Spatial sampling quantile point pattern analysis was defined and applied to the Glebe data set. The analysis showed that the highest lead concentrations were strongly clustered. The comparison of this data set with the simulation confidence limits of a Poisson process, a short-radius clustered point process and a geostatistical simulation showed a random process for the third quartile of lead concentrations but strong clustering for the data in the upper quartile. Thus the distribution of topsoil lead concentrations over Glebe may have resulted from several contamination processes, mainly from regular or random processes with large diffusion ranges and short-range clustered processes for the hot spots. Point patterns with the same characteristics as the Glebe experimental pattern could be generated by separate additive geostatistical simulation. Spatial sampling quantile point patterns statistics can, in an easy and accurate way, be used complementarily with geostatistical methods. Copyright © 2005 John Wiley & Sons, Ltd. [source] Development of a computerized assessment for visual maskingINTERNATIONAL JOURNAL OF METHODS IN PSYCHIATRIC RESEARCH, Issue 2 2002Michael Foster Green Abstract Visual masking provides a highly informative means of assessing the earliest stages of visual processing. This procedure is frequently used in psychopathology research, most commonly in the study of schizophrenia. Deficits in visual masking tasks appear to reflect vulnerability factors in schizophrenia, as opposed to the symptoms of the illness. Visual masking procedures are typically conducted on a tachistoscope, which limits standardization across sites, as well as the number of variables that can be examined in a testing session. Although visual masking can be administered on a computer, most methods used so far have had poor temporal resolution and yielded a limited range of variables. We describe the development of a computerized visual masking battery. This battery includes a staircase procedure to establish an individual's threshold for target detection, and a relatively dense sampling of masking intervals. It includes both forward and backward masking trials for three different masking conditions that have been used previously in experimental psychopathology (target location, target identification with high-energy mask, and target identification with low-energy mask). Copyright © 2002 Whurr Publishers Ltd. [source] Physical properties of rocks from the upper part of the Yaxcopoil-1 drill hole, Chicxulub craterMETEORITICS & PLANETARY SCIENCE, Issue 6 2004Y. Popov Thermal conductivity, thermal diffusivity, density, and porosity were measured on 120 dry and water-saturated rocks with a core sampling interval of 2,2.5 m. Nondestructive, non-contact optical scanning technology was used for thermal property measurements including thermal anisotropy and inhomogeneity. Supplementary petrophysical properties (acoustic velocities, formation resisitivity factor, internal surface, and hydraulic permeability) were determined on a selected subgroup of representative samples to derive correlations with the densely measured parameters, establishing estimated depth logs to provide calibration values for the interpretation of geophysical data. Significant short- and long-scale variations of porosity (1,37%) turned out to be the dominant factor influencing thermal, acoustic, and hydraulic properties of this post impact limestone formation. Correspondingly, large variations of thermal conductivity, thermal diffusivity, acoustic velocities, and hydraulic permeability were found. These variations of physical properties allow us to subdivide the formation into several zones. A combination of experimental data on thermal conductivity for dry and water-saturated rocks and a theoretical model of effective thermal conductivity for heterogeneous media have been used to calculate thermal conductivity of mineral skeleton and pore aspect ratio for every core under study. The results on thermal parameters are the necessary basis for the determination of heat flow density, demonstrating the necessity of dense sampling in the case of inhomogeneous rock formations. [source] Influence of habitat discontinuity, geographical distance, and oceanography on fine-scale population genetic structure of copper rockfish (Sebastes caurinus)MOLECULAR ECOLOGY, Issue 13 2008M. L. JOHANSSON Abstract The copper rockfish is a benthic, nonmigratory, temperate rocky reef marine species with pelagic larvae and juveniles. A previous range-wide study of the population-genetic structure of copper rockfish revealed a pattern consistent with isolation-by-distance. This could arise from an intrinsically limited dispersal capability in the species or from regularly,spaced extrinsic barriers that restrict gene flow (offshore jets that advect larvae offshore and/or habitat patchiness). Tissue samples were collected along the West Coast of the contiguous USA between Neah Bay, WA and San Diego, CA, with dense sampling along Oregon. At the whole-coast scale (~2200 km), significant population subdivision (FST = 0.0042), and a significant correlation between genetic and geographical distance were observed based on 11 microsatellite DNA loci. Population divergence was also significant among Oregon collections (~450 km, FST = 0.001). Hierarchical amova identified a weak but significant 130-km habitat break as a possible barrier to gene flow within Oregon, across which we estimated that dispersal (Nem) is half that of the coast-wide average. However, individual-based Bayesian analyses failed to identify more than a single population along the Oregon coast. In addition, no correlation between pairwise population genetic and geographical distances was detected at this scale. The offshore jet at Cape Blanco was not a significant barrier to gene flow in this species. These findings are consistent with low larval dispersal distances calculated in previous studies on this species, support a mesoscale dispersal model, and highlight the importance of continuity of habitat and adult population size in maintaining gene flow. [source] Nonparametric confidence intervals for Tmax in sequence-stratified crossover studiesPHARMACEUTICAL STATISTICS: THE JOURNAL OF APPLIED STATISTICS IN THE PHARMACEUTICAL INDUSTRY, Issue 1 2008Susan A. Willavize Abstract Tmax is the time associated with the maximum serum or plasma drug concentration achieved following a dose. While Tmax is continuous in theory, it is usually discrete in practice because it is equated to a nominal sampling time in the noncompartmental pharmacokinetics approach. For a 2-treatment crossover design, a Hodges,Lehmann method exists for a confidence interval on treatment differences. For appropriately designed crossover studies with more than two treatments, a new median-scaling method is proposed to obtain estimates and confidence intervals for treatment effects. A simulation study was done comparing this new method with two previously described rank-based nonparametric methods, a stratified ranks method and a signed ranks method due to Ohrvik. The Normal theory, a nonparametric confidence interval approach without adjustment for periods, and a nonparametric bootstrap method were also compared. Results show that less dense sampling and period effects cause increases in confidence interval length. The Normal theory method can be liberal (i.e. less than nominal coverage) if there is a true treatment effect. The nonparametric methods tend to be conservative with regard to coverage probability and among them the median-scaling method is least conservative and has shortest confidence intervals. The stratified ranks method was the most conservative and had very long confidence intervals. The bootstrap method was generally less conservative than the median-scaling method, but it tended to have longer confidence intervals. Overall, the median-scaling method had the best combination of coverage and confidence interval length. All methods performed adequately with respect to bias. Copyright © 2007 John Wiley & Sons, Ltd. [source] | ||||||||||