Proving two different universal machine types give equivalent results in original Solomonoff induction paper

Solomonoff's original paper about Solomonoff induction contains the following (p. 18):

Suppose $$M$$ to be a universal machine with binary input alphabet, and an output alphabet that is the same as that of $$T$$ [where $$T$$ is a string of length $$m$$]. We shall consider $$M$$ to be either of the ordinary type, $$M_1$$, described in Section 3.1, or the 3-tape type, $$M_2$$, described in Section 3.2. In the present case, it has been proved that these two machine types give equivalent results.

Consider all binary strings of length $$R$$. Say $$N_R$$ of them are meaningful inputs to $$M$$—i.e., they cause $$M$$ to stop eventually. Of these $$N_R$$ meaningful inputs to $$M$$, say $$N_T$$ of them result in outputs whose first $$m$$ symbols are, respectively, identical to the $$m$$ symbols of $$T$$. Then the a priori probability assigned to $$T$$ will be $$N_T/N_R \tag{9}$$ This ratio will become more exact as $$R$$ approaches infinity, but will usually be good enough if $$R$$ satisfies Eq. (8).

It can be proved that the present inductive inference model is identical to that of Section 3.2, if $$M$$ is a machine of either type $$M_1$$ or of type $$M_2$$.

In this paper, $$M_1$$ is a universal Turing machine, and "$$M_2$$ is a 3-tape machine with unidirectional output and input tapes." (I think nowadays this would be called a universal monotone Turing machine.)

The inductive inference model of Section 3.2 is to set the probability of $$T$$ to $$\sum_{i=1}^\infty 2^{-N(T,i)}$$, where $$N(T,i)$$ is the number of bits in the $$i$$th minimal program for $$T$$ (i.e. outputs something starting with $$T$$, and if you remove the final bit from the program it will no longer output something starting with $$T$$).

My questions are:

1. What does Solomonoff mean when he says "it has been proved" and "It can be proved"? As far as I can tell, the paper itself does not contain these proofs. Does he mean that he proved these privately but chose not to include them in the paper?
2. I think I've found a proof that using expression (9) for $$M_2$$ gives the same probability as the inductive inference model of Section 3.2. But I have no idea how using expression (9) with $$M_1$$ gives the same answer (as Solomonoff claims). How can I prove this? I find $$M_1$$ programs difficult to work with, since not all programs halt and since $$M_1$$ is not monotonic (i.e. a longer program can come and erase output produced by a shorter version of that program).

It is known that for any partial function that can be computed by any $$k$$-tape machine (such as a universal monotone Turing machine $$M_2$$), there exists is a regular single-tape universal Turing machine that computes the same partial-function (see e.g., WP page on Multitape TMs). Thus, for Eq. (9), one can consider either a universal monotone $$M_2$$, or a regular UTM $$M_1$$ that emulates the same $$M_2$$.
However, it is not generally true that for any UTM $$M_1$$, there is a monotone Turing machine $$M_2$$ that computes the same partial function as $$M_1$$ (for example, choose any $$M_1$$ whose halting set is not prefix-free. The halting set of any monotone machine must be prefix-free, so there cannot be such a machine that emulates $$M_1$$). Thus, it seems somewhat misleading for Solomonoff to say that $$M_1$$ and $$M_2$$ are fully equivalent.
In modern AIT (e.g., Li and Vitanyi), $$\sum_{i=1}^{\infty} 2^{-N(T,i)}$$ is always computed using an UTM $$T$$ whose halting set is prefix-free (as would be guaranteed by a universal monotone UTM, not just any UTM).