### Table 1. Products to be grouped.

1999

"... In PAGE 10: ....1.0.42) on a HP C-200 workstation. The starting assemblies as listed in Table1 were used for the DPG formation. Diverse starting assemblies were chosen to illustrate the utility of the methodology.... ..."

### Table 1: Sources of uncertainty [Huijbregts, 1998] covered in this work.

"... In PAGE 2: ...95)/Xi(0.05) (3) Sources of uncertainty included in the assessment are shown in Table1 . Concerning LCI flows, uncertainty and different sources of variability are depicted as one single generic dispersion factor per group of flows.... In PAGE 2: ... Six and nine LCIs were compared for the production of benzene and sodium hydroxide, respectively [Geisler, 2004]. Calculating dispersion factors for comparable elementary flows in these different LCIs yields information on all sources of uncertainty in the LCI assessed here ( Table1 ). A specific model uncertainty in the LCI stems from the use of the estimation procedure for LCIs of chemical production-processes in the supply of the active substances and formulation ingredients [Geisler, 2003b]: Knowledge on the efficiency of production processes is uncertain.... In PAGE 3: ...worst-case scenario ( Table1 ) [Geisler, 2003b]. Finally, for those LCI data acquired specifically for this study (e.... ..."

### Table 1: Average Uncertainty in Inferring Volumes from Boundary Groups and Faces

1997

"... In PAGE 7: ...4 To compare the utility of boundary groups versus faces in recovering volumes, we will use the conditional probabilities captured in the augmented aspect hierarchy to de ne a measure of average inferencing uncertainty, or the degree to which uncertainty remains in volume identity given a recovered boundary group or face. More formally, we de ne average inferencing uncertainty for boundary groups, UBG Avg, and for recovered faces, UF Avg, as follows:5 UBG Avg = ? 1 NBG NBG Xi=1 NV X j=1 P rob(Vj j BGi) log P rob(Vj j BGi) (2) UF Avg = ? 1 NFA NF Xi=1 NV X j=1 P rob(Vj j Fi) log P rob(Vj j Fi) (3) where: NBG = number of boundary groups in the augmented aspect hierarchy NFA = number of faces in the augmented aspect hierarchy NV = number of volumes in the augmented aspect hierarchy Table1 compares the average inferencing uncertainty for the boundary groups and faces. Clearly, faces o er a more powerful focus feature for the recovery of volumetric parts than do the simpler features that make up the boundary groups.... ..."

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### Table 1: Average Uncertainty in Inferring Volumes from Boundary Groups and Faces

1997

"... In PAGE 8: ...4 To compare the utility of boundary groups versus faces in recovering volumes, we will use the conditional probabilities captured in the augmented aspect hierarchy to de ne a measure of average inferencing uncertainty, or the degree to which uncertainty remains in volume identity given a recovered boundary group or face. More formally, we de ne average inferencing uncertainty for boundary groups, UBG Avg, and for recovered faces, UF Avg, as follows:5 UBG Avg = ? 1 NBG NBG Xi=1 NV X j=1 P rob(Vj j BGi) log P rob(Vj j BGi) (2) UF Avg = ? 1 NFA NF Xi=1 NV X j=1 P rob(Vj j Fi) log P rob(Vj j Fi) (3) where: NBG = number of boundary groups in the augmented aspect hierarchy NFA = number of faces in the augmented aspect hierarchy NV = number of volumes in the augmented aspect hierarchy Table1 compares the average inferencing uncertainty for the boundary groups and faces. Clearly, faces o er a more powerful focus feature for the recovery of volumetric parts than do the simpler features that make up the boundary groups.... ..."

Cited by 48

### Table 1: Average Uncertainty in Inferring Volumes from Boundary Groups and Faces

1997

"... In PAGE 7: ...4 To compare the utility of boundary groups versus faces in recovering volumes, we will use the conditional probabilities captured in the augmented aspect hierarchy to de ne a measure of average inferencing uncertainty, or the degree to which uncertainty remains in volume identity given a recovered boundary group or face. More formally, we de ne average inferencing uncertainty for boundary groups, UBG Avg, and for recovered faces, UF Avg, as follows:5 UBG Avg = ? 1 NBG NBG Xi=1 NV X j=1 P rob(Vj j BGi) log P rob(Vj j BGi) (2) UF Avg = ? 1 NFA NF Xi=1 NV X j=1 P rob(Vj j Fi) log P rob(Vj j Fi) (3) where: NBG = number of boundary groups in the augmented aspect hierarchy NFA = number of faces in the augmented aspect hierarchy NV = number of volumes in the augmented aspect hierarchy Table1 compares the average inferencing uncertainty for the boundary groups and faces. Clearly, faces o er a more powerful focus feature for the recovery of volumetric parts than do the simpler features that make up the boundary groups.... ..."

Cited by 48

### Table 2: Probability (%) of the quotient of impact scores to be larger than one, with asterisks designating significant differences between the products and contribution to variance (CTV) of groups of parameters.

"... In PAGE 3: ... 4 RESULTS 4.1 Case-Study Results In Table2 , the probability of the quotient of impact scores (Equation 1) to be larger than one and the uncertainty ranges are shown. The spreads in the distributions are caused by uncertainty in LCI flows and in characterisation factors.... In PAGE 3: ... Significant differences between the two products occur only in the worst- case scenario, with regard to acidification, photooxidant creation and human toxicity impacts: Moddus shows significantly higher impact scores than Stuntan according to the significance criterion chosen (see Methods). The applied doses of the two plant-protection products have high contributions to variance in all impact categories ( Table2 ), because the applied dose is the reference flow of the functional unit. Therefore, uncertainty in this parameter has an effect on all other parameters in the life cycles compared.... In PAGE 3: ... With regard to single substances, the characterisation factor for emissions of chlorocholine chloride to air and water has high contributions to variance in freshwater ecotoxicity impact-scores. This contribution to variance of impacts of chlorocholine chloride explains the large uncertainty range in freshwater ecotoxicity in Table2 , because the generic uncertainty factors for the characterisation factors of chlorocholine chloride are as high as 50 (emission to air) and 100 (emission to water) [Geisler 2003a]. Additionally, air emissions of substrates in chemical production exhibit a considerable contribution to variance in the worst-case scenario, where the emission factor for such substances is relatively high [Geisler, 2003b].... ..."

### Table 5 Poverty indicators by household participation in different institutional types Mean

"... In PAGE 33: ... To eliminate the regional effect, we thus restricted the comparing households to Yatenga). As shown in Table5 , when looking at participating vs. nonparticipating households (without distinguishing among different kinds of LLIs), the lower poverty among participating households results from higher mean expenditure per capita.... ..."

### Table 10. Flow Condition Uncertainty Based on Quoted Instrument Uncertainty

1995

"... In PAGE 15: ... The repeatability of these flow parameters is summarized in table 9 where the mean, sample standard deviation, and 95-percent prediction interval are given for each parameter for the combined short-term groups of polars; figure 17 shows the varia- tions from polar to polar within each combined short- term group. Table10 presents the variation expected due to pure instrument uncertainty for the four repeated flow conditions included in this investigation; the uncertainty of the measured quantities is that described herein and in reference 18, and the uncertainty of the calculated quan- tities is based on the propagation of uncertainty equations given by Rind.... ..."

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