Projection bias

Table 4 shows the projected anomalies of annual and seasonal precipitation and air temperature for the Ebro River, whereas Figs. 7-12 show the spatial variation of these anomalies, computed using ordinary kriging. Anomalies are computed as the difference between projected bias-corrected values for the climate scenarios (January 2071 to December 2100) and the corresponding values observed during the control period (January 1961 to December 1990), and they can be viewed as expected values about which uncertainties of different origin exist. Table 4 shows that both RCMs predict a reduction in the mean annual precipitation, accompanied by an increase in the mean annual temperature with respect to the control period. In particular, the RCAO E model projects a reduction of 21.8% for the mean annual precipitation and an increase of +6.3°C for the mean annual temperature. 

Since die FTR will be fixed in size, the criticality, design parameter is fissile enrichment. FTR des models overestimate the required enrichment. The projected bias, in terms of mass of fissile material is 4 %, but, based upon analyses of four FTR criticals, this number is uncertain by 1%. Hence, the calculated enrichment is reduced 0.5% due to the bias and increased 1% to ensure the required excess reactivity. 

Projection bias Assuming (unconsciously) chat others resemble you in critical respects—thoughts, positions, values. 

Illusion of transparency, projection bias, and false consensus bias... 

The projection bias may have made it impossible for the treatment team to believe that the patient wanted what she had requested because it was not a choice they could imagine wanting themselves under the same circumstances. Wishful thinking may also have distorted the treatment team s judgment they wanted to make the decision that would allow the patient to live. 

DEGRAD STABILjcIs Section 1.8.4 The analysis of stability reports often suffers from the fact that the data for each batch of product is scrutinized in isolation, which then results in a see-no-evil attitude if the numerical values are within specifications. The analyst is in a good position to first compare all results gained under one calibration (usually a day s worth of work) irrespective of the products/projects affected, and then also check the performance of the calibration samples against experience, see control charts, Section 1.8.4. In this way, any analytical bias of the day will stand out. For this purpose a change in format from a Time-on-Stability to a Calendar Time depiction is of help. 

Incorporated in a device, the LPCVD -Si H material shows electroluminescence only in reverse bias [673]. The mechanism is similar to the one described for c-Si. The PECVD a-Si H material was incorporated in a p-i-n solar cell structure, with a thickness of the intrinsic layer of 500 nm (see Section 1.11.1). Oxygen was coimplanted at 80 keV (3.2 x 10 O/em-) and at 120 keV (5.5 x lO 0/cm ), which resulted in a roughly constant oxygen concentration of 1.0% in the Er projected range in the middle of the intrinsic a-Si H layer. Electroluminescence is observed under forward bias [674]. 

Figure 20 shows more definitively how the location and orientation of a hyperplane is determined by the projection directions, a and the bias, o- Given a pattern vector x, its projection on the linear discriminant is in the a direction and the distance is calculated as d(x ) / cf The problem is the determination of the weight parameters for the hyper-plane ) that separate different pattern classes. These parameters are typically learned using labeled exemplar patterns for each of the pattern classes. 

One can identify two major categories of uncertainty in EIA data (scientific) uncertainty inherited in input data (e.g., incomplete or irrelevant baseline information, project characteristics, the misidentification of sources of impacts, as well as secondary, and cumulative impacts) and in impact prediction based on these data (lack of scientific evidence on the nature of affected objects and impacts, the misidentification of source-pathway-receptor relationships, model errors, misuse of proxy data from the analogous contexts) and decision (societal) uncertainty resulting from, e.g., inadequate scoping of impacts, imperfection of impact evaluation (e.g., insufficient provisions for public participation), human factor in formal decision-making (e.g., subjectivity, bias, any kind of pressure on a decision-maker), lack of strategic plans and policies and possible implications of nearby developments (Demidova, 2002). 

The Csv symmetry of 14 was reflected in its five-line 13C NMR spectrum. X-ray crystallographic analysis revealed further that the all-equatorial conformation is adopted in the solid state (Fig. 3-1). This bias persists as well in solution and may be a consequence of more favorable dipole-dipole interactions. The parallelism between 14 and its isomer 24, whose ground-state conformer also projects all three C-0 bonds in the equatorial plane (Fig. 3-2), is striking.8... 

It should also be noted that there is a strong conformational bias for only one of the product chelate conformers. For example, erythro chelate D should be strongly disfavored by both 1,3-diaxial Rj L and CH3 Xq steric control elements. Consequently, it is assumed that the transition states leading to either adduct will reflect this conformational bias. Further support for these projections stems from the observations that the chiral acetate enolates derived from 149a exhibit only poor diastereoface selection. In these cases the developing Rj CH3 interaction leading to diastereomer A is absent. Similar transition state allylic strain considerations also appear to be important with the zirconium enolates, which are discussed below. 

In the traditional interpretation of the Fangevin equation for a constrained system, the overall drift velocity is insensitive to the presence or absence of hard components of the random forces, since these components are instantaneously canceled in the underlying ODF by constraint forces. This insensitivity to the presence of hard forces is obtained, however, only if both the projected divergence of the mobility and the force bias are retained in the expression for the drift velocity. The drift velocity for a kinetic interpretation of a constrained Langevin equation does not contain a force bias, and does depend on statistical properties of the hard random force components. Both Fixman and Hinch nominally considered the traditional interpretation of the Langevin equation for the Cartesian bead coordinates as a limit of an ordinary differential equation. Both authors, however, neglected the possible existence of a bias in the Cartesian random forces. As a result, both obtained a drift velocity that (after correcting the error in Fixman s expression for the pseudoforce) is actually the appropriate expression for a kinetic interpretation. 

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