Uncertainty major sources

Appraisal activity should be based upon the information required. The first step is therefore to determine what uncertainties appraisal is trying to reduce, and then what information is required to tie down those uncertainties. For example, if fluid contacts are a major source of uncertainty, drilling wells to penetrate the contacts is an appropriate tool seismic data or well testing may not be. Other examples of appraisal tools are ... 

The choice of the location for well A should be made on the basis of the position which reduces the range of uncertainty by the most. It may be for example, that a location to the north of the existing wells would actually be more effective in reducing uncertainty. Testing the appraisal well proposal using this method will help to identify where the major source of uncertainty lies. 

The major source of error In calculating the free energies of Pu0(g) and Pu02(g) from Battles et al. probably results from the derived equations for the partial pressures of 0(g) and Pu(g) as a consequence of uncertainties In Ionization cross sections. The thermodynamic assessments of Ackermann et al. Involve extrapolations of oxygen potentials reported by Markin and Rand (4) In temperature of the order of 500 K. However, a second and third... 

Unlike non-radiometric methods of analysis, uncertainty modelling in NAA is facilitated by the existence of counting statistics, although in principle an additional source of uncertainty, because this parameter is instantly available from each measurement. If the method is in a state of statistical control, and the counting statistics are small, the major source of variability additional to analytical uncertainty can be attributed to sample inhomogeneity (Becker 1993). In other words, in Equation (2.1) ... 

A major source of error in most measurements is the presence of impurities in the sample. The effect of an impurity depends upon its amount in the sample and upon the difference between its density and the density of the principal constituent. Even when the sample purity is provided quantitatively, the impurities often are not identified individually. Nevertheless, a report of sample purity reduces the estimated uncertainty because it can be taken as evidence that the investigator has considered sample purity. The most ubiquitous impurity in liquids is water, and, because its density differs significantly from those of hydrocarbons, it is a common source of error. Exclusion of water requires that the sample be protected from the atmosphere during transfer, and that special precautions be taken to remove the sample from containers. 

In order to obtain a feeling for the major sources of uncertainty and error in the calculation of reaction rate constants, it is useful to consider the nature of the errors inherent in the measurement of these parameters. 

The major sources of uncertainty relating to the method should be identified. Those contributions not used in the final calculation, because they are considered insignificant, should be mentioned. The overall uncertainty should be listed, together with an explanation of how it was derived. A more detailed treatment may be in a cross-referenced hie. 

As shown in Table 11.1, hydrothermal emissions are a major source of soluble iron, manganese, and zinc and a minor source of aluminum, cobalt, copper, and lead. Other elements with significant hydrothermal inputs include lithium, rubidium, cesium, and potassium. Considerable uncertainty also surroimds these flux estimates because they are the result of extrapolations from measurements made at a small number of hydrothermal systems at single points in time. These fluxes appear to vary significantly over short time scales as tectonic activity abruptly opens and closes cracks in the oceanic crust. 

Baird et al. (1996) suggested a probabilistic alternative to the practice used by the US-EPA to derive RfDs from a NOAEL and application of UFs. The probabilistic approach expresses the human population threshold for a given substance as a probability distribution of values, rather than a single RfD value, taking into account the major sources of scientific uncertainty in such estimates. The approach was illustrated by using much of the same data that US-EPA used to justify their RfD procedure. For the four key extrapolations that were considered necessary to define the human population threshold based on animal data (interspecies, interindividual, LOAEL-to-NOAEL, and subchronic-to-chronic), the proposed approach used available data to define a probability distribution of each adjustment factor, rather than using available data to define point estimates of UFs. 

The reviewer is led to a melancholy conclusion. If the theory used to correct for anharmonicity is questionable, and the data are never sufficient to supply overproof, then anharmonicity remains a major source of uncertainty. Indeed, since corrections due to anharmonicity are as large as the errors caused by neglecting the distinction between CO force constants and parameters, there seems little point, as far as the CO vibrations are concerned, in attempting the fuller force field analysis at all. 

Another important reason for using multiple scenarios is to represent major sources of variability, or what-if scenarios to examine alternative assumptions about major uncertainties. This can be less unwieldy than including them in the model. Also, the distribution of outputs for each separate scenario will be narrower than when they are combined, which may aid interpretation and credibility. A special case of this occurs when it is desired to model the consequences of extreme or rare events or situations, for example, earthquakes. An example relevant to pesticides might be exposure of endangered species on migration. This use of multiple scenarios in ecological risk assessment has been termed scenario analysis, and is described in more detail in Ferenc and Foran (2000). 

Systematically identify, evaluate, and incorporate the major sources of uncertainty, including model uncertainty. Initially, all potentially significant routes of exposure and types of effect should be included. Identify models to represent these processes. Use sensitivity analysis to identify insignificant variables, exposure routes, and effects. 

The shot noise is the major source of uncertainty. The shot noise over a bandwidth Afis, according to Eq. (11.6),... 

There are two major sources of uncertainty in the BAG-MP4 heats of formation. First, there are imcertainties resulting from incomplete knowledge of the appropriateness of the chosen theoretical methods for a given molecule. Second, systematic imcertainties exist that result from the lack of good reference compounds needed to estabhsh the bond additivity corrections. The magnitude of the first is estimated using an ad hoc method developed previously that uses the results from lower-level calculations (Table 1). 

There is a need today to quantify the effects of aerosol sources on ambient particulate matter loadings. Identifying the major sources of ambient particulate matter loadings was a fairly simple process when values exceeded 500 /ig/m and stack emissions were plainly visible. Control of these emitters was forthcoming and effective. At levels of 150 to 200 fxg/w , the use of annual emission inventories focused further regulatory efforts on major sources which have resulted in more successful reductions. Presently, at levels around 75-100 /ig/m, the uncertainties involved in these assessments of source contributions are greater than the contributions themselves. 

One approach to elucidating the contribution of natural variability to recent temperature trends is to examine markers for temperature over much longer time scales, prior to the industrial revolution. A major source of such data is ice cores (see also Section B.2a). These ice cores provide a record of climate and atmospheric composition for at least 110,000 years, for which there is agreement among various studies. Data are available for 250,000 years before the present (bp), but there is some uncertainty in the dating of the layers corresponding to these older ice core depths (Chappel-laz et al., 1997). 

The hazard assessment identifies the adverse effects that a chemical may cause and investigates the relationship between their magnitude and the dose to which an organism is exposed. A major source of uncertainty is the use of data from tests on laboratory animals (or plants) to investigate toxicity to other species (including humans). There are at least four reasons why there is uncertainty in the application of test data to exposures of humans and wild animals (RCEP, 2003, pp21—22 Rodricks, 1992, ppl58-179) ... 

The top-down approach is often used when there are method validation data from properly conducted interlaboratory studies, and when the laboratory using reproducibility as the measurement uncertainty can demonstrate that such data are applicable to its operations. Chapter 5 describes these types of studies in greater detail. In assigning the reproducibility standard deviation, sR, to the measurement uncertainty from method validation of a standard method, it is assumed that usual laboratory variables (mass, volume, temperature, times, pH) are within normal limits (e.g., 2°C for temperature, 5% for timing of steps, 0.05 for pH). Clause 5.4.6.2 in ISO/ 17025 (ISO/IEC 2005) reads, In those cases where a well-recognized test method specifies limits to the values of the major sources of uncertainty of measurement and specifies the form of presentation of the calculated results, the laboratory is considered to have satisfied this clause by following the test method and reporting instructions. ... 

Diagrams can be used to illustrate the relationships described by the conceptual model and risk hypotheses. Conceptual model diagrams are useful tools for communicating important pathways and for identifying major sources of uncertainty. These diagrams and risk hypotheses can be used to identify the most important pathways and relationships to consider in the analysis phase. The hypotheses considered most likely to contribute to risk are identified for subsequent evaluation in the risk assessment. 

The first round of key comparisons in these fields was largely completed by the end of 1999 and showed that gas analysis, elemental analysis and pH measurement are already rather well developed and mature areas, whereas organic analysis, for example in the clinical and food areas, requires more attention, in particular with respect to sampling and sample pretreatment which are often the major sources of uncertainty. As regards sampling which is even more important when field measurements are linked up with national or international standards, comprehensive practical and theoretical knowledge is available, especially for particulate material sampling [7], which can be used where applicable to improve the comparability of chemical measurement results. 

Solvent Composition. The use of reactivity data obtained from systems containing non-aqueous solvents is probably the major source of uncertainty in individual rate constants, and hence in the tabulated values of log(ks/kjj2o)-... 

Assessment of the impact of the use of gas in the First World War on the Western, Eastern and Italian Fronts is difficult. Analysis of casualty figures is doomed to failure because of a contemporary lack of definition and classification. Gas casualty estimates by several national sources exceed a million but elements of uncertainty exist on the precise cause of death or major source of injury in those who were both gassed and wounded. Also comparison of gas and other battlefield injuries shows vast swings in the proportions on different fronts in different years (Table 2.2). 

In general one can conclude that the enthalpies of solution of the metals form the major source of uncertainty. Cordfunke and Konings (2001b) tried to overcome this by combining results from different sources and by inter- or extrapolation values as a function of the molarity, which was possible in some cases because accurate determinations of the enthalpy of solution as a function of molarity were performed by Merli et al. (1998). But in some cases (e.g., the cerium trihalides) the analysis heavily relies almost completely on a single measurement. 

On examinination of the results of the integral-reactor studies, one or more sources of uncertainty were found in each case, which makes the kinetic conclusion drawn from them doubtful. The three major sources of uncertainty are (1) the use of a method which is insensitive to the precise functional forms of the kinetics, (2) the presence of diffusion-transport effects which modify the kinetics, and (3) the presence in the cumene used of strong inhibitors of the cracking reaction. 

D-QSAR. Since compounds are active in three dimensions and their shape and surface properties are major determinants of their activity, the attractiveness of 3D-QSAR methods is intuitively clear. Here conformations of active molecules must be generated and their features captured by use of conformation-dependent descriptors. Despite its conceptual attractiveness, 3D-QSAR faces two major challenges. First, since bioactive conformations are in many cases not known from experiment, they must be predicted. This is often done by systematic conformational analysis and identification of preferred low energy conformations, which presents one of the major uncertainties in 3D-QSAR analysis. In fact, to date there is no computational method available to reliably and routinely predict bioactive molecular conformations. Thus, conformational analysis often only generates a crude approximation of active conformations. In order to at least partly compensate for these difficulties, information from active sites in target proteins is taken into account, if available (receptor-dependent QSAR). Second, once conformations are modeled, they must be correctly aligned in three dimensions, which is another major source of errors in the system set-up for 3D-QSAR studies. 

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