Table 16 Sample Clauses

Table 16. Performance Management Roles and Responsibilities

Table 16. 8.1(d)-2

Table 16. Description of a possible E&T programme on nuclear structural and fuel materials, as envisioned by the EERA JPNM.

Table 16. Number (N) of measured and modelled pairs for each area and selected day. 53 Table 17. Deviations of simulated versus observed climate variables for 2010/08/07 and 2010/08/08 in Casco Viejo. 61 Table 18. Difference between measured and modelled cClimate variables for 2010/08/06 and 2010/08/08 in Miribilla. 65 Table 19. Description of PET values derived from measured climate variables data for each area and selected day. 69 Table 20. PET correlation coefficients between measured and modelled values for each area and selected day, as well as for all data available. 71 Table 21. Values of adjusted R squared from the seven regression analysis of measured PET with the independent variables involved. 72 Table 22. Values of standardized regression coefficients (Beta) from the seven regression analysis of measured PET. 72 Table 23. Values of adjusted R squared from the seven regression analysis of ΔPET (PETmeasured - PETmodelled) with the independent variables involved. 73 Table 24. Values of standardized regression coefficients (Beta) from the seven regression analysis of ΔPET (PETmeasured - PETmodelled). 73 Table 25. Values of ΔPET, ΔPETa, ΔPETb values for each area and selected day. 74 Table 26. Values of adjusted R-squared from the three regression analysis of ΔPET, ΔPETa, ΔPETb with the independent variables involved. 74 Table 27. Values of standardized regression coefficients (Beta) from the three regression analysis of ΔPET, ΔPETa, ΔPETb. 74 CMIP5 coupled model intercomparison project phase 5 CORDEX coordinated regional climate downscaling experiment CPU central processing unit (computer processor) ECMWF European centre for medium-range weather forecasting ENVI-met microscale urban climate model Enviro-HIRLAM environment – high resolution limited area model GCM global climate model IPCC integovernmental panel on climate change (of the UN) SW, ▇▇ ▇▇▇▇▇- and Longwave (radiation) Tmrt mean radiant temperature UHI urban heat island UrbClim urban boundary layer climate model WS, WD wind speed and direction Within RAMSES, use is made of numerical models to generate urban climate projections. This is a useful and even necessary exercise as only such computer models can provide quantitative information regarding the impact of climate change on local urban climate. Indeed, since cities strongly shape their own climate, when assessing climate change impacts it is insufficient to simply take output, such as temperature or precipitation, from Global Climate Models (GCM...

Table 16. 1 Table 16. 2

Table 16. The household income ranges between the two legal status groups were different, with undocumented immigrants reporting lower income ranges than their documented counterparts. Male respondents also skewed towards slightly higher incomes than females, regardless of legal status (Table 17). Over two-thirds of undocumented immigrants reported incomes in the range of $501 to $3,500 a month, with a modal income of $1,501-2,000/month. Documented immigrants on the other hand, reported income ranges that were higher, with a mode of $2,501- 3,500. A higher frequency of undocumented immigrant females reported making under $1,500/month (n=21), less than their documented counterparts. A similar pattern was seen with undocumented immigrant men with n=13 of them reporting making less than $1,500/month, while only one documented male reported making under $1,500/month. It is important to note that the survey population was not a random sample and that the 3:1 proportion of undocumented to documented survey respondents may have contributed to the income distributions. Undocumented immigrants rarely reported having health insurance coverage, while just over half of documented immigrants reported having health insurance (Table 18). A mere 6.8 percent (n=5) of undocumented immigrant females and 12.0 percent (n=9) of undocumented males reported having some type of health insurance, much lower than documented females (51.6 percent; n=16) and males (56.5 percent; n=13). Of the 43 respondents with health insurance, there does not seem to be much of a difference in health insurance satisfaction between legal status groups; though a higher frequency of documented immigrants reported having health coverage that “Always” met their medical and health needs (Table 19). The most common types of health insurances reported in households were Medicaid, Private Insurance, and Peachcare (Table 20). Table 20 clearly suggests that Medicaid is an important resource for undocumented respondents; 87.5 percent of those who have someone in their home with Medicaid were undocumented respondents. Undocumented immigrants reported going longer without having visited a medical or health professional than documented respondents (Table 21). The most reported time period without seeing a medical or health professional was more than 1 year (n=88); undocumented immigrants accounted for 86.4 percent of this group and were younger. Additionally, there is a notable difference amongst females 35-50 years of age;...

Table 16. Media component Interfaces Section 6.6.1 will elaborate on the different usage for these two types of access to the orchestration template.

Table 16. “Reduced risk” irrigation target values over the growing season for midday stem water potential (bars). Period Month March April May June July August September Early- -6 -8 -9 -10 -12 -13 -14 Mid- -7 -8 -9 -11 -12 -13 -15 Late- -7 -9 -10 -11 -12 -14 -15 pressure chamber readings and following the “reduced risk” recommendation of irrigation scheduling. Monitored sites generally observed a good match between the observed and the target SWP. An example of these comparisons can be seen if Figure 12. -0.6 -0.8 -1.0 -1.2 -1.4 treatment Conventional Reduced Risk Target ▇▇▇▇▇▇ APR MAY JUN JUL AUG SEP 0 -1 -2 -3 CSUC -0.6 -0.8 -1.0 -1.4 APR MAY JUN JUL AUG SEP Giacolini APR MAY JUN JUL AUG SEP -0.5 -0.7 -0.9 -1.1 -1.3 -1.5 -1.7 -1.9 ▇▇▇▇ APR MAY -0.6 -0.8 -1.0 -1.2 -1.4 JUN JUL AUG SEP ▇▇▇▇▇▇▇ APR MAY JUN JUL AUG SEP -0.6 -0.4 -0.8 -0.8 -1.0 -1.2 -1.2 -1.4 -1.6 -1.6 ▇▇▇ ▇ -2.0 ▇▇▇▇▇▇▇ ▇▇ APR MAY JUN JUL AUG SEP APR MAY JUN JUL AUG SEP -0.5 -0.5 -1.0 -1.0 -1.5 ▇▇▇▇▇▇▇ -1.5 ▇▇▇▇▇ J APR MAY JUN JUL AUG SEP APR MAY JUN JUL AUG SEP -0.5 TARGET -0.9 OBSERVED -1.3 -1.7 Willow G APR MAY JUN JUL AUG SEP

Table 16. Species (and their abundances) that best explain dissimilarities between the estuary groups defined by the LINKTREE analysis. Cont: contribution of each species to the dissimilarity between groups. Cum: Cumulative contributions.