### Table 6. Distance to springs; nests and feeding locations (distances in meters).

"... In PAGE 8: ...able 5. Third order habitat selection at the Redding study site .........................20 Table6... In PAGE 18: ... Nesting amp; Feeding Areas We found a total of 14 nests between the two study areas, 9 at the Quincy site and 5 at the Redding site. These nests were in fairly close proximity to known mineral sites ( Table6 ). Of the 14 nests, 6 fledged one young, 2 were unsuccessful and 6 were of unknown fate (Appendix B).... In PAGE 18: ... Feeding areas ranged anywhere from 50 m to 28 km from the known mineral sites. Mean distances from feeding areas to mineral sites in the Redding area averaged 4 km while in the Quincy area distances averaged a little over 5 km from known mineral sites ( Table6... ..."

### Table 2. Distances between the convergence points and the low order string and a known optimum. modal. The peak that occurs in the low range of Hamming distances corresponds to the di erences between convergence points that were global optima. The local optima form the second set of larger Hamming distances. The Hamming distances between convergence points indicates that the SGA and GSAT were converging to points that were very di erent from one another. This behavior is consistent with the argument that the MAXSAT landscape is very at. Essentially, there is little information to guide the search in any predictable way. The next question to ask is whether or not the SGA convergence points were consistent with the low order schema information.

1998

"... In PAGE 9: ... The Global Target strings were the strings used to create each of the random problem instances. Table2 lists the average dis- tances distances between the convergence points and the L-String and Global Target. The column All lists the di erence between the L-String and Global Target compared to the set of all convergence points.... ..."

Cited by 4

### Table 2. Distances of small mammal movements in the study area.

"... In PAGE 2: ... Distances and directions of small mammals movements During the study, colored fibers were found in 361 individuals, 31% of all of the animals caught. The distribution of the distances moved by the two most numerous species is presented in Table2 . For both species the longest routes exceeded 1000 m; there was little tendency for the numbers of Apodemus to diminish with distance covered.... ..."

### Table 10: The distance distribution for Random Direction model in Scenar- ios 1, 2, and 3

"... In PAGE 40: ... As can be seen from the table, there is practically no difference in dis- tance distribution between Scenarios 1, and 2. The distance distributions of random direction ( Table10 and Gauss-Markov (Table 11 show the same sit- uation. Thus we can conclude that variance in speed has at most little effect to distance distribution with these mobility models.... ..."

### Table 11: The distance distribution for Gauss-Markov model in Scenarios 1, 2, and 3

"... In PAGE 40: ... As can be seen from the table, there is practically no difference in dis- tance distribution between Scenarios 1, and 2. The distance distributions of random direction (Table 10 and Gauss-Markov ( Table11 show the same sit- uation. Thus we can conclude that variance in speed has at most little effect to distance distribution with these mobility models.... ..."

### Table 10: The distance distribution for Random Direction model in Scenar- ios 1, 2, and 3

"... In PAGE 40: ... As can be seen from the table, there is practically no difference in dis- tance distribution between Scenarios 1, and 2. The distance distributions of random direction ( Table10 and Gauss-Markov (Table 11 show the same sit- uation. Thus we can conclude that variance in speed has at most little effect to distance distribution with these mobility models.... ..."

### Table 11: The distance distribution for Gauss-Markov model in Scenarios 1, 2, and 3

"... In PAGE 40: ... As can be seen from the table, there is practically no difference in dis- tance distribution between Scenarios 1, and 2. The distance distributions of random direction (Table 10 and Gauss-Markov ( Table11 show the same sit- uation. Thus we can conclude that variance in speed has at most little effect to distance distribution with these mobility models.... ..."

### Table 3: Example sense inventory for bank An erroneous classi cation between close siblings in the sense hierarchy should be given relatively little penalty, while misclassi cations across homographs should receive a much greater penalty. The penalty matrix distance(subsense1; subsense2) could capture simple hierarchical distance (e.g. (Resnik, 1995; Richardson et al., 1994)), derived from a single se- mantic hierarchy such as WordNet, or be based on a weighted average of simple hierarchical distances from multiple sources such as sense/subsense hierar- chies in several dictionaries. A very simple example of such a distance matrix for the bank sense hierarchy is given in Table 4.

1997

Cited by 61

### Tables 9-11 and 9-12 suggest that there is little difference in performance between Monte Carlo search GHC algorithms and Monte Carlo search SGHC algorithms. Overall, Tables 9- 7 through 9-10 suggest that the SGHC algorithms outperform the GHC algorithms. The minimum distance found over the R replications using SGHC algorithms is significantly smaller than the minimum distance found over the R replications using GHC algorithms for both the simulated annealing and pure local search algorithms. Additionally, the standard deviation of the optimal values over the R replications is much smaller using the SGHC algorithms.

2000