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This is equivalent to taking the logarithm (to a given base) of the raw values, taking the arithmetic mean of those values and then back-transforming to get the geometric mean As we saw with equations 31 and 32, using the logarithms of the numbers gives us an additive rather than a multiplicative model Thus, Nt+1 = Nt is transformed to log Nt+1 = log + log Nt If we look at the pdf of log10 we see that the arithmetic mean is zero (see Fig 37, where the bars are symmetrical about zero) and therefore, on average, log Nt+1 = log Nt Back-transforming (taking the antilog) gives the geometric mean of 1 Models of population extinction have used these ideas to estimate probabilities of extinction and mean times to extinction (or, conversely, persistence time) for a wide range of species.



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Instead of building tables to denormalize a join, a view can be created that can select the two primary keys from the joined tables. A clustered index created on the view stores the valid data from the primary-key and foreign-key pairs. While a normal view only stores the SQL select statement and the data isn t materialized until the view is called, an indexed view stores a copy of the data in a clustered index. Clustered indexes merge the data page and the b-tree index leaf to store the actual data in the physical order of the index. The clustered index uses a view as a framework to define the columns to be stored. Numerous restrictions exist on indexed views, including the following: The ANSI null and quoted identifier must be enabled when the view is created, and when any connection attempts to modify any data in the base tables. The index must be a unique clustered index; therefore, the view must produce a unique set of rows without using distinct. This can be a problem because situations that need denormalizing include most many-to-many relationships, which tend to produce duplicate rows in the result set. For this reason, most indexed views only span one-tomany relationships. The tables in the view must be tables (not nested views) in the local database and must be referenced by means of the two-part name (owner.table). The view must be created with the option with schema binding.





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000101010 001101010 010010101 011001100 100110011 101000111 110111000 111011011 Observe that the rst 3 bits are uniquely prescribed, so each chromosome is guaranteed to be different from all others in the population. Perhaps this approach is not the best, since it only guarantees that the rst gene or so is different. A variation is to prescribe some other combination of bits. For example, if the chromosomes are three genes made up of 3 bits each, the rst (or some other) bit in each gene could be set, as in 111111111 100100000 111011100 100000011 000100100 000100000 010010101 011001011 Either approach guarantees an initial population of unique chromosomes but doesn t ensure that identical chromosomes are not evaluated in later generations. Some GA users advocate populations with all unique members (Michalewicz, 1992). New members of the population (offspring or mutated chromosomes) are checked against the chromosomes in the current population. If the new chromosome is a duplicate, then it is discarded. This approach requires searching the population for each new member. Searching the population for identical twins is only worth the effort if the cost function evaluation takes longer than the population search. A new generation consists of nonmutated parents, mutated parents, offspring, and mutated offspring. If there is an identical chromosome that already has an associated cost, then this cost is assigned to the new chromosome. Otherwise, the cost must be calculated from the cost function. An alternative is to not allow identical chromosomes to mate, therefore saving some computer time while helping maintain diversity in the population.

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Foley (1994) used the density-independent model ln Nt+1 = ln + ln Nt, assuming that ln was normally distributed with a variance vr and mean 0, and that the population started at size N0 and proceeded on a random walk with a maximum value of k and a minimum value of 0: extinction The mean time to extinction, Te, is then given as: Te = ( 2 ln ( N 0 ) vr ) ( ln ( k ) ln ( N 0 ) 2) This model was applied to different species, showing times to extinction of 19 237 years for ve populations of the wolf (Canis lupus) and 1378 6107 years for six populations of the mountain lion (Felis concolor) It would be unwise to accept these and other estimates of Te or probability of extinction as absolute estimates.

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