out of memory when using Distributed

Is unclear to me why the following runs out of memory. The yaxarray that I'm using is not that big. Is it being copied/transfer to each worker? If so, it seems to be inefficient and using a similar approach to SharedArrays[is not like that already? 😕] probably will be better, if it is remotely an option.
Example adapted from here.

using Distributed
addprocs(7)

@everywhere using Pkg
@everywhere Pkg.activate(".")
@everywhere using YAXArrays
@everywhere using Statistics
@everywhere using Zarr
@everywhere function mymean(output, pixel)
       output = mean(pixel)
end

axlist = [
    RangeAxis("time", range(1, 20, length=2000)),
    RangeAxis("x", range(1, 10, length=200)),
    RangeAxis("y", range(1, 5, length=200)),
    CategoricalAxis("Variable", ["var1", "var2"])]
data = rand(2000, 200, 200, 2);
ds = YAXArray(axlist, data)

indims = InDims("Time")
outdims = OutDims()

resultcube = mapCube(mymean, ds, indims=indims, outdims=outdims)

Start by running the provided Julia example with Distributed, YAXArrays, and the mapCube call to reproduce the memory growth. Investigate whether the YAXArray or its data is copied to each worker, compare that behavior with the SharedArrays approach mentioned in the issue, and document or address the confirmed cause.