Generating expectation curves

Figure 6.9 Expectation curve for an exploration prospect 6.2.3 Generating expectation curves...

Suppose that four wells have been drilled in a field, and the geologist has identified three possible top sands maps based on the data available. These maps, along with the ranges of data for the other input parameters (N/G, S, cj), B ) have been used to generate an expectation curve for STOMP. 

The MALDI triple-quadrupole mass spectrometer generates calibration curves with linearity and dynamic ranges similar to those typically expected from a triple-quadrupole mass spectrometer. In most cases, linearity is established over three orders of magnitude with suitable accuracy and precision. Figure 11.5 depicts calibration curves obtained for some common drugs using neat standards. 

The evaluation of linearity can be best described as the characterization of the test method response curve. A plot of the test method response against analyte concentration is often expected to be linear over a specified range of concentrations. Some assays generate nonlinear curves. 

Fig. 9-3 Conceptual model to describe the interaction between chemical weathering of bedrock and down-slope transport of solid erosion products. It is assumed that chemical weathering is required to generate loose solid erosion products of the bedrock. Solid curve portrays a hypothetical relationship between soil thickness and rate of chemical weathering of bedrock. Dotted lines correspond to different potential transport capacities. Low potential transport capacity is expected on a flat terrain, whereas high transport is expected on steep terrain. For moderate capacity, C and F are equilibrium points. (Modified with permission from R. F. Stallard, River chemistry, geology, geomorphology, and soils in the Amazon and Orinoco basins. In J. I. Drever, ed. (1985), "The Chemistry of Weathering," D. Reidel Publishing Co., Dordrecht, The Netherlands.)...

Run a set of standards of four or more concentration levels covering the expected range of residues. Generate a calibration curve for each analyte and obtain a linear regression with a correlation coefficient of at least 0.90 for each analyte. Do not use any sample run data if the combined regression for the standards run immediately before, during and after the samples does not meet this criterion. 

In the first example, the predicted oral absorption for a series of ACE inhibitors has been compared with published values of human bioavailability. For the generation of calculated absorption, a sigmoidal curve between observed human absorption and PSA for a series of reference compounds was used [25], The predicted oral absorption for ACE inhibitors is plotted against the calculated PSA values is shown in Fig. 19.6 however, as expected, only a partial correlation existed between predicted absorption and observed in vivo bioavailability. 

It is seen that the diameters of bronchioles (averaged over generations 11 - 15) vary little with age. The increase in bronchial size is greater, but still less than might be expected if airways are simply scaled for overall body dimensions (illustrated by the dashed curves in Figure 9, which are functions of body weight W). Since bronchiolar diameter does not change much with age it is likely that the thickness of bronchiolar epithelium is also relatively constant. However, in the case of the bronchi, it is reasonable to assume that epithelial thickness is proportional to bronchial diameter. Thus, it is necessary to use age dependent conversion factors between the surface density of alpha-decays and dose to cells. 

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