General-Review-(post-GW190814).md

@@ -81,6 +81,7 @@ marginalizing to compute posteriors and odds ratio
   * [ ] inference produces strictly larger estimates for P(m2<Mmax) when spins are included (but known to be less than the break-up spin) compared to when spins are neglected
     - Review statement: I have checked this and it is true only for GW200105. Results can be found in the first two columns of the second table in "Comparison to Previous Results" below. The descrepancy between GW200105 and GW200115 is likely because of the fact that the secondary spin posteriors on GW200115 are very broad and therefore a significant fraction (~30%) of the posterior support is above the maximum allowed spin (approximately 0.7). This causes P(m_2<Mmax|data) to occasionally be lower for the case in which we are considering spins. On the other hand, secondary spin posteriors on GW200105 are narrower and peaked at lower spins (rarely above 0.7). 
     - This should be checked by doing a run with the low-spin priors and seeing if the --spin-column results are strictly larger than the others
+    - This should also be checked with a fresh install and specified samples.
   * [X] scaling of Mmax(Mtov, Rtov, spin) was correctly implemented based on Eqn 18 of [Breu+Rezzolla 2016](https://academic.oup.com/mnras/article/459/1/646/2608837).
     - Review statement: I have cross checked the function `br2016_mmax_scaling()` in `bin/mmax_model_selection/utils.py` with equation 18 in the above paper. There seems to be an error. Namely, the second term in the mmax expression should have a factor of the compactness squared, but it is currently to the 1st power. 
     - Updated review statement: This has now been corrected by Reed in commit 50fe99a74e8fa3ebf183505a59af13e053afac36 of MR !9.