What are these 2.5% and 97.5%? I didn't understand what is written in the documentation. Anyone could explain to me in a more practical terms?
A 95% confidence interval (CI) is in fact an algorithm with the following property:
Suppose you re-run an experiment many, many times (from sampling from the population over measuring your data to fitting your model) and estimate the same parameter of interest with an associated 95% CI. Then, because of noise in your data, you will get a different parameter estimate and a different 95% CI each time. But 95% of these different CIs will cover the true parameter value.
(Yes, this is unintuitive, and frequently gotten wrong. Here is a recent discussion.)
Now, of course a 95% CI is not unique. It consists of two end points. You could in each case calculate a lower 1% limit (such that when you repeat the experiment many times, the true parameter value will be lower than this 1% limit in only 1% of cases) and an upper 96% limit (i.e., out of your many experiments, the true parameter value will be higher than this limit in 4% of cases). The two together will yield an interval that covers the true parameter value in 95% of cases, so this is a 95% CI.
However, symmetry is appealing. So the "standard" 95% CI consists of a lower 2.5% limit and an upper 97.5% limit. When you repeat your experiment many times, the true parameter value will be below the CI in 2.5% of cases and above it in another 2.5% of cases - and the CI will cover it in 95% of cases.
The numbers you see are the lower (2.5%) and upper (97.5%) limits of this standard symmetric CI for your three estimated parameters in the single run of the experiment you are analyzing right now.
The Wikipedia page is somewhat confusing, but there is a lot of material there.