Forcing the most specialized iterator

For the DifferentialEquations.jl iterator, it seems that

using OrdinaryDiffEq

f_2dlinear = (du,u,p,t) -> du.=1.01u
prob = ODEProblem(f_2dlinear,rand(2,2),(0.0,1.0))
sol1 =solve(prob,Tsit5())
using IterableTables, DataFrames
df = DataFrame(sol1)

works, but

using OrdinaryDiffEq

f_2dlinear = (du,u,p,t) -> du.=1.01u
prob = ODEProblem(f_2dlinear,rand(2),(0.0,1.0))
sol1 =solve(prob,Tsit5())
using IterableTables, DataFrames
df = DataFrame(sol1)

doesn't go through the iterator in DiffEqBase, but instead uses a generic one for a matrix (since the solution is a VectorOfArray{Vector} and is thus 2 dimensional, while the first case is 3 dimensional). This seems odd because I would've assumed it would pick the more specialized iterator that's only for DESolution, and I'm not sure where the dispatch logic on this is taking place.

Reproduce both DataFrame(sol1) examples and compare the iterator selected for the 2D and 3D solutions. Trace the DiffEqBase DESolution iterator and the generic matrix iterator to locate the dispatch decision; done means the cause of the differing selection is identified and the intended specialized path is verified.