### Table 2 Supervised cell clustering of log10 runoff (m3/year), wave height, tidal range, chlorophyll a, average sea-surface temperature, and minimum salinity with reef occurrence statistics

### Table 3. : Error statistics for salinity simulations by FATHOM for the MFL base case model with monthly measurements over the period 1991 through 2002. (adapted with permission from Cosby, et al 2005).

2006

"... In PAGE 35: ... However, the grid that includes the mangrove zone is described as better in predicting the low frequency variation in water surface level in the northeast part of Florida Bay, including episodic events such as tropical storms. As shown by Table3 , both grids (nominal coast model = NM, wetland model = WM) perform well in predicting salinity. According to Tetra Tech, Inc.... In PAGE 35: ... The smaller grid configuration (NM) apparently predicts better because groundwater is excluded, and there are problems depicting some mangrove zone features such as the Buttonwood embankment. Compared to daily MLR salinity models (Tables 1 and 2), monthly FATHOM model ( Table3 ), and the USGS SICS model (see below), daily salinity simulations by EFDC to-date contain significantly greater uncertainty (error) than the other 3 model systems. It is noted that the EFDC model development activity is on-going.... In PAGE 39: ... The SICS model has also been used for making daily salinity simulations near the coastal creeks that are being monitored by the USGS. Calibration statistics are presented in Table3 from Langevin, et al 2004b. The SICS model was found to be better at simulating monthly salinity values (r2 = 0.... ..."

### Table 2: Bayesian model averaging, Bayesian model selection, and constrain-based results for an analysis of whether \X causes Z quot; given data summarized in Table 1. number of output of output of

1997

"... In PAGE 11: ... Table 1: A summary of data used in the example. number su cient statistics of cases x y z x yz xy z xyz x y z x yz xy z xyz 150 5 36 38 15 7 16 23 10 250 10 60 51 27 15 25 41 21 500 23 121 103 67 19 44 79 44 1000 44 242 222 152 51 80 134 75 2000 88 476 431 311 105 180 264 145 The rst two columns in Table2 shows the results of applying Equation 4 under the assumptions stated above for the rst N cases in the data set. When N = 0, the data set is empty, in which case probability of hypothesis h is just the prior probability of \X causes Z quot;: 8/25=0.... In PAGE 11: ...32. Table2 shows that as the number of cases in the database increases, the probability that \X causes Z quot; increases monotonically as the number of cases increases. Although not shown, the probability increases toward 1 as the number of cases increases beyond 2000.... In PAGE 11: ... Although not shown, the probability increases toward 1 as the number of cases increases beyond 2000. Column 3 in Table2 shows the results of applying Bayesian model selection. Here, we list the causal relationship(s) between X and Z found in the model or models with the highest posterior probability p(mjD).... In PAGE 11: ... Two of the models have Z as a cause of X; and one has X as a cause of Z. Column 4 in Table2 shows the results of applying the PC constraint-based causal discov- ery algorithm (Spirtes et al., 1993), which is part of the Tetrad II system (Scheines et al.... ..."

Cited by 54

### Table 2: Bayesian model averaging, Bayesian model selection, and constrain-based results for an analysis of whether \X causes Z quot; given data summarized in Table 1. number of output of output of

1997

"... In PAGE 11: ... Table 1: A summary of data used in the example. number su cient statistics of cases x y z x yz xy z xyz x y z x yz xy z xyz 150 5 36 38 15 7 16 23 10 250 10 60 51 27 15 25 41 21 500 23 121 103 67 19 44 79 44 1000 44 242 222 152 51 80 134 75 2000 88 476 431 311 105 180 264 145 The rst two columns in Table2 shows the results of applying Equation 4 under the assumptions stated above for the rst N cases in the data set. When N = 0, the data set is empty, in which case probability of hypothesis h is just the prior probability of \X causes Z quot;: 8/25=0.... In PAGE 11: ...32. Table2 shows that as the number of cases in the database increases, the probability that \X causes Z quot; increases monotonically as the number of cases increases. Although not shown, the probability increases toward 1 as the number of cases increases beyond 2000.... In PAGE 11: ... Although not shown, the probability increases toward 1 as the number of cases increases beyond 2000. Column 3 in Table2 shows the results of applying Bayesian model selection. Here, we list the causal relationship(s) between X and Z found in the model or models with the highest posterior probability p(mjD).... In PAGE 11: ... Two of the models have Z as a cause of X; and one has X as a cause of Z. Column 4 in Table2 shows the results of applying the PC constraint-based causal discov- ery algorithm (Spirtes et al., 1993), which is part of the Tetrad II system (Scheines et al.... ..."

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### Table 3. Regression surface model parameters for the six GAP-Model qmos types Type

"... In PAGE 8: ... Hence, the diagnostic statistics sug- gest that a quadratic model better represents the curvature in the I-MOS and S-MOS response surfaces than a linear model. Table3 shows the significant (non-zero) quadratic regression model parameters for the six GAP-Model qmos types, whose general representation is given as follows: qmos = C0 + C1bnet + C2dnet + C3lnet + C4jnet +C5l2 net + C6j2 net + C7dnetlnet + C8lnetjnet (6) V. Framework Implementation and its Application The salient components and workflows of the GAP-Model based framework were described briefly in Section 1 using the illustration shown in Figure 2.... ..."

### Table 2: Bayes factors, posterior probabilities, Bayesian LM statistics and classical Likelihood Ratio tests for the UK and Danish data.

1997

"... In PAGE 33: ...re restricted in the cointegration space, i.e. 0 ? = 0 and 0 ? 2 = 0, which means that the vector (t DOUTt)0 is added to the Yt?1 vector and that 0 becomes a (4 6) matrix. The rst part of Table2 displays the results of a Bayesian cointegration analysis for the model (92). In the rst row the results for a model without the dummy variables DOILt and DOUTt are reported.... In PAGE 33: ...olumns. These tests indicate no cointegration relation at a 5% level of signi cance. On basis of the Bayesian LM statistics we even opt for two cointegration relations. The results change if we include the dummy variables DOUTt and DOILt like in Hendry and Doornik (1994), see second column of Table2 . The posterior probabilities now also indicate two cointegration relations between the series in Yt.... In PAGE 35: ... Hence, the 0 matrix and the Y?1 matrix are extended with an extra column. The second part of Table2 displays the results of a Bayesian cointegration analysis for the Danish data. The results are based on a di use (Je reys apos;) prior for the parameters and equal prior probabilities (53) Pr[rank = r] = Pr[rank = n], r = 0; : : : ; n.... In PAGE 35: ... The Bayes factors favour every rank reduction over a full rank model and lead to 100% posterior probability for a model with one cointegration relation. The fourth column of Table2 display the outcomes of the Bayesian LM statistics. Only the LM(3j4) and LM(2j4) statistics lie inside the 95% HPD interval, which implies that two cointegration relations between mt, id t , ib t and yt are plausible.... ..."

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### Table 2. The Sea Surface Wind Speed and Direction Measured by NOAA Moored Buoys Within the AVHRR SST Image Shown in Figure 1aa

"... In PAGE 2: ... The critical SI values, below which the sea-breeze circulation occurs, have been experimentally determined to be between 3 and 10 for different test sites near large lakes and islands [Biggs and Graves, 1962; Lyons and Olsson, 1972]. [10] For the case examined here, wind measurements, taken about 20 minutes before the NOAA satellite pass by four NOAA moored buoys and one Coastal-Marine Auto- mated Network (C-MAN) station, within the region covered by the satellite SST image are given in Table2 . The surface wind speed is between 6.... ..."

### Table 2. Comparison of Modeled and Measured BC in Snow, Sea-Ice, and Precipitation

"... In PAGE 6: ... Incident flux is direct beam from 60C176 zenith angle. For comparison of these BC concentrations with global observa- tions and model predictions, see Table2 and Figures 4 and 5. D11202 FLANNER ET AL.... In PAGE 7: ...3. Measured and Modeled BC Concentrations in Snow [39] Table2 summarizes measurements of present-day BC in snowpack from all studies known to the authors. When mean values are not reported in the original literature, we report means of all published measurements from each location.... In PAGE 7: ... [2002] applied an acid-base/thermal method. Table2 also shows CAM/SNICAR predictions of BC concentrations in the surface snow layer. Data in the lower portion of the table show BC concentrations in precipitation, with model esti- mates derived from wet deposition and precipitation rates.... In PAGE 8: ...Figure 4. Model versus observed BC concentrations in near-surface snow for data from Table2 , grouped by region (precipitation measurements excluded). Model data are from the top 2 cm of snowpack.... In PAGE 9: ..., 1994]. Measurements in Table2 are not time resolved, except for Slater et al. [2002], who reported elemental carbon (EC, often considered synonymous with BC) con- centrations with quarter-annual resolution, varying from 4 to 30 ng gC01 over the course of two years.... ..."

### Table 6. Comparison between the calibration sample log salinity mean and standard error

1992

"... In PAGE 13: ... Note that the four regression models associated with this quarter section appeared to have good prediction resolution and prediction reliability. Table6 compares the calibration sample log salinity mean and standard error estimates to the regression based estimates for this same quarter section. These data demonstrate the usefulness of the regression modeling approach.... ..."

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