Employing Hilbert-Schmidt measure, we explicitly compute and analyze a number of determinantal product (bivariate) moments |ρ| k |ρ P T | n , k, n = 0, 1, 2, 3, . . ., P T denoting partial transpose, for both generic (9-dimensional) two-rebit (α = 1 2 ) and generic (15-dimensional) two-qubit (α = 1) density matrices ρ. The results are, then, incorporated by Dunkl into a general formula (Appendix D 6), parameterized by k, n and α, with the case α = 2, presumptively corresponding to generic (27-dimensional) quaternionic systems. Holding the Dyson-index-like parameter α fixed, the induced univariate moments (|ρ||ρ P T |) n and |ρ P T | n are inputted into a Legendre-polynomialbased (least-squares) probability-distribution reconstruction algorithm of Provost (Mathematica J., 9, 727 (2005)), yielding α-specific separability probability estimates. Since, as the number of inputted moments grows, estimates based on the variable |ρ||ρ P T | strongly decrease, while ones employing |ρ P T | strongly increase (and converge faster), the gaps between upper and lower estimates diminish, yielding sharper and sharper bounds. Remarkably, for α = 2, with the use of 2,325 moments, a separability-probability lower-bound 0.999999987 as large as 43, 195302 (2010)).