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wopen(t)=1+a tβw_{\text{open}}(t) = 1 + a\,t^{\beta}

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which approaches a fixed value of 1 + a/b as t grows without bound, and wopen(t)=1+a tβw_{\text{open}}(t) = 1 + a\,t^{\beta}. for an exponent β\beta between 0 and 1, which keeps rising — more and more slowly, but without any asymptote — for every value of t . Wiser and colleagues derived the second form theoretically from a model combining clonal interference with diminishing-returns epistasis among beneficial mutations, and reported that across 50,000 generations of real data, “mean fitness appears to increase without bound, consistent with a power law” [ 2 ] . The decelerating rate that Lenski’s 1991 paper had already observed over the first 2,000 generations turns out to be compatible with no ceiling at all: a…

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wopenw_{\text{open}}

Symbol w_open

wow_open is part of the quantity the equation computes from the expression on the right.

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Published contexts (1)

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wopen(t)=1+a tβw_{\text{open}}(t) = 1 + a\,t^{\beta}

Equation 4 · Evolutionary Biology

Seventy-Five Thousand Generations of E. coli, and Counting

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

which approaches a fixed value of 1 + a/b as t grows without bound, and wopen(t)=1+a tβw_{\text{open}}(t) = 1 + a\,t^{\beta}. for an exponent β\beta between 0 and 1, which keeps rising — more and more slowly, but without any asymptote — for every value of t . Wiser and colleagues derived the second form theoretically from a model combining clonal interference with diminishing-returns epistasis among beneficial mutations, and reported that across 50,000 generations of real data, “mean fitness appears to increase without bound, consistent with a power law” [ 2 ] . The decelerating rate that Lenski’s 1991 paper had already observed over the first 2,000 generations turns out to be compatible with no ceiling at all: a…

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