Accuracy evaluation potential approaches

To bypass these potential problems, we manually browsed a small subset of the literature for papers that actually use computational tools. Rather than read through all the papers in the selected journals, a random subset of papers was examined to see which used computational chemistry techniques. To test the accuracy of this approach, the results obtained from one-quarter of the total number of issues in a given year was compared with the results found by browsing all issues in that volume. The test case was the 1994 volume of Journal of Organic Chemistry. The random selection yielded 14%, whereas inspection of all 1376 papers also yielded 14%. Thus browsing a fraction of the issues of a journal should suffice for our purposes. At least one-fourth of the total number of issues of the journals listed in Table 1 were read, but one-third to one-half were evaluated for the smaller journals. 

Clearly, the successful reproduction of the experimental result is, in part, related to the high quality of the potential energy surface. A more direct evaluation of the accuracy of transition-state theory can be obtained via a comparison to other (more exact) theoretical approaches to the calculation of the rate constant, all using the same potential energy surface. Table 6.3 shows such a comparison. We observe that transition-state theory does overestimate the rate constant but the agreement is quite reasonable, especially when the simplicity of the calculation is taken into account. 

Although human exposure data are essential for accurate evaluation of an agent s risk potential, data of sufficient quality and quantity are frequently unavailable. Thus, there is uncertainty in the exposure component of the evaluative process, even as there is in hazard characterization. When toxicity data indicate the potential for an adverse effect, the need to estimate the nature of human exposure becomes imperative. In those instances, exposure estimates can be derived using modeling approaches based on data from other sources, and one or more default assumptions can be used. The greater the number of default assumptions employed, the greater the uncertainty about the accuracy of the expert judgment. 

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