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Frequency-dependent selection may explain the high degree of polymorphism in the MHC. [13] In behavioral ecology, negative frequency-dependent selection often maintains multiple behavioral strategies within a species. A classic example is the Hawk-Dove model of interactions among individuals in a population.
In the absence of mutation or heterozygote advantage, any allele must eventually either be lost completely from the population, or fixed, i.e. permanently established at 100% frequency in the population. [2] Whether a gene will ultimately be lost or fixed is dependent on selection coefficients and chance fluctuations in allelic proportions. [3]
The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]
Selection is a genetic operator in an evolutionary algorithm (EA). An EA is a metaheuristic inspired by biological evolution and aims to solve challenging problems at ...
Frequency-dependent foraging is defined as the tendency of an individual to selectively forage on a certain species or morph based on its relative frequency within a population. [1] Specifically for pollinators , this refers to the tendency to visit a particular floral morph or plant species based on its frequency within the local plant ...
Here the independent variable is the dose and the dependent variable is the frequency/intensity of symptoms. Effect of temperature on pigmentation: In measuring the amount of color removed from beetroot samples at different temperatures, temperature is the independent variable and amount of pigment removed is the dependent variable.
Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...
The first and most common function to estimate fitness of a trait is linear ω =α +βz, which represents directional selection. [1] [10] The slope of the linear regression line (β) is the selection gradient, ω is the fitness of a trait value z, and α is the y-intercept of the fitness function.