### Table 2. Weed control effects of glyphosate* and imazapyr* on railway embankments after different spraying strategies. SD = standard deviation. N = number of field experiments

### Table 1: Average number of genes out of the 120 with the smallest p-values identified in common based on analyzing sub- samples of the IHF data set.

"... In PAGE 12: ... With the exception of the two statistics based on ratios of means (standard deviation for the ratio of raw means for 2 by 2, ratio of raw means for 3 by 3, ratio of mean of logs for 2 by 2 and ratio of mean of logs for 3 by 3 were 25, 32, 26, and 13 respectively), standard deviations over comparisons were generally small and between 7 and 13. The different combinations of window size and hyperparameter seemed to have little effect on the consistency of the Bayesian approach so only outcomes corresponding to the best and worst parameter combinations are presented in Table1 . The comparisons among statistical approaches presented in Table 2 were generated in a similar manner to those described above.... In PAGE 18: ... Similarly, in the case of the comparisons of size three, 36 measures of consistency were made from comparisons having one replicate in common. Table1 summarizes the consistency of the different statistical approaches. As might be expected additional replications of an experiment result in greater consistency at identifying the same set of genes as being up- or down-regulated.... In PAGE 20: ...The Bayesian approach allows the identification of more true positives with fewer replicates The data in Table1 show that additional experimental replications result in the identification of a more consistent set of up- or down- regulated genes, and that the Bayesian statistical approach identifies a more consistent set than a simple t-test. The natural question that arises is whether these genes are true positives.... ..."

### Table 4. Overview of search formulation modes

"... In PAGE 6: ...Table4 pre- sents an overview of search formulation modes. Form-filling templates are the common mode of search formulation.... ..."

### Table 1: Factors influencing the formulation of PSPs

"... In PAGE 7: ... The problem part should encapsulate the knowledge regarding (i) the features of all existing projects for which various procurement systems were employed; (ii) the characteristics and needs of the clients; and (iii) the properties of the external environment that encountered by clients. Luu et al (2003) summarised the factors that could influence the choice of the PSPs ( Table1 ). The solution part focuses on the knowledge pertinent to the PSPs, their weightings adopted in previous CPS evaluations, and reasons/justifications for previous solutions.... In PAGE 22: ... 20 LIST OF CAPTIONS Figure 1: Knowledge pertinent to PSPs formulation stage Figure 2: Knowledge pertinent to procurement selection stage Figure 3: Basic mechanism of retrieving and reusing the stored knowledge Figure 4: Interface for extracting the characteristics of client, project and environment Figure 5: Possible values for the attribute of client experience Figure 6: Cases retrieved during PSPs formulation stage Figure 7: Solution adopted in the previous case during PSPs formulation stage Figure 8: Interface for altering the PSPs and weightings at procurement selection stage Figure 9: Solution and outcome of the retrieved cases at procurement selection stage Figure 10: Critic-based adaptation at procurement selection stage Table1 : Factors influencing the formulation of PSPs Table 2: Common PSPs ... ..."

### Table 2: Common PSPs

"... In PAGE 8: ... 6 of the PSPs used in the previous CPS processes - these PSPs being related to time certainty, cost certainty, speed, flexibility, responsibility, complexity, price competition, risk allocation and quality as illustrated in Table2 (see Sidwell, 1984; NEDO, 1985; Nahapiet and Nahapiet, 1985; Skitmore and Marsden, 1988; Walker, 1989; Hughes, 1989; Masterman, 1992; Masterman and Gameson; 1994; Love et al, 1998; Rowlinson and McDermott, 1999; Ambrose and Tucker, 1999; Alhazmi and McCaffer, 2000; Chen, 2000; Kumaraswamy and Dissanayaka, 2001). lt; Figure 2 gt; lt; Table 2 gt; The knowledge in the solution part should, however, contain the procurement system used, and its sub-managerial systems, such as the tendering method and contractual arrangement, etc.... In PAGE 22: ... 20 LIST OF CAPTIONS Figure 1: Knowledge pertinent to PSPs formulation stage Figure 2: Knowledge pertinent to procurement selection stage Figure 3: Basic mechanism of retrieving and reusing the stored knowledge Figure 4: Interface for extracting the characteristics of client, project and environment Figure 5: Possible values for the attribute of client experience Figure 6: Cases retrieved during PSPs formulation stage Figure 7: Solution adopted in the previous case during PSPs formulation stage Figure 8: Interface for altering the PSPs and weightings at procurement selection stage Figure 9: Solution and outcome of the retrieved cases at procurement selection stage Figure 10: Critic-based adaptation at procurement selection stage Table 1: Factors influencing the formulation of PSPs Table2 : Common PSPs ... ..."

### Table 2: Uni ed formulation of reconstruction methods Methods Basis i(x) Bias A(x)

"... In PAGE 39: ... For all basis function methods, there is no bias term; that is, A(x) 0. See Table2 for a summary. Here the Bayesian methods are implemented by taking the logarithm of the posterior probability in Eqs.... ..."

### Table 5. MDL and Bayesian

2002

"... In PAGE 4: ...(7) from [3] [4]. The experimental results, as shown in Table5 , confirmed that the model selection using our Bayesian criterion re- sulted in better word recognition rates compared with that using the MDL criterion, especially in the case of small amounts of training data. Table 4.... ..."

Cited by 4

### Table 5. MDL and Bayesian

2002

"... In PAGE 4: ...(7) from [3] [4]. The experimental results, as shown in Table5 , confirmed that the model selection using our Bayesian criterion re- sulted in better word recognition rates compared with that using the MDL criterion, especially in the case of small amounts of training data. Table 4.... ..."

Cited by 4

### Table 1 Formulations of EFPA

2007

"... In PAGE 4: ... 1. Table1 defines each formulation of Table 1 Formulations of EFPA... ..."

### Table 4. Comparison of Bayesian active learning and Bayesian immediate learning on Proflle 83. Bayesian Bayesian

2003

"... In PAGE 6: ...g., Table4 ). This improvement is partly due to the proflle (term and term weight) learning algorithm, which also beneflts from the additional training data generated by the active learner.... ..."

Cited by 6