# AIC corrected likelihood ratio?

In a 2012 answer to a question on model selection, a user proposed the following (log-base-2) likelihood ratio with "AIC correction":

#compute the AIC-corrected log-base-2 likelihood ratio (a.k.a. "bits" of evidence)
(AIC(mod1)-AIC(mod2))*log2(exp(1))


I found the approach interesting, but could not find any documentation for it, and not being a statistician myself, I feel unsure in using it.

So my questions are:

1. Can anybody provide a source for this approach? Edit: This article provides a very similar equation: exp(-1/2 * (AIC(mod1)-AIC(mod2))) Maybe someone with a bit more calculus than me could explain how to get from this formula to the original one in the question?

2. Does it provides an advantage on simple log likelihood ratio? (e.g. weighting for model complexity?) The same article defines it as evidence ratio, thus exactly how many times mod2 is more evident than mod1.

3. What does the *log2(exp(1)) bit means?

Note: the referred article is "AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons" by Burnham, Anderson & Huyvaert, 2011.

log2(exp(1))=1.44269504089

exp(-1/2)=0.60653065971