Update README

Author: WrathfulSpatula

README.md

@@ -49,7 +49,7 @@ The `find_a_factor()` function should return any nontrivial factor of `to_factor
 - `wheel_factorization_level` (default value: `11`): "Wheel" vs. "gear" factorization balances two types of factorization wheel ("wheel" vs. "gear" design) that often work best when the "wheel" is only a few prime number levels lower than gear factorization. Optimized implementation for wheels is only available up to `13`. The primes above "wheel" level, up to "gear" level, are the primes used specifically for "gear" factorization. Wheel factorization is also applied to "peturb" `FACTOR_FINDER` mode into non-multiples on the wheel, if the level is set above `1` (which might not actually work, but we leave it for your experimentation).
 - `sieving_bound_multiplier` (default value: `1.0`): This controls the sieving bound and is calibrated such that it linearly multiplies the square root of the number to factor (for each `1.0` increment). While this might be a huge bound, remember that sieving termination is primarily controlled by when `gaussian_elimination_row_multiplier` is exactly satisfied.
 - `smoothness_bound_multiplier` (default value: `1.0`): This controls smoothness bound and is calibrated such that it linearliy multiplies `exp(0.5 * std::sqrt(log(N) * log(log(N))))` for `N` being the number to factor (for each `1.0` increment). This was a heuristic suggested by Elara (an OpenAI custom GPT).
-- `gaussian_elimination_row_multiplier` (default value: `1.2`): This controls the number of rows sieved for Gaussian elimination before terminating the sieve and is calibrated such that it linearly multiplies the number of smooth prime columns in the Gaussian elimination matrix (for each `1.0` increment). So long as this setting is appropriately low enough, `sieving_bound_multiplier` can be set basically arbitrarily high.
+- `gaussian_elimination_row_multiplier` (default value: `1.0`): This controls the number of rows sieved for Gaussian elimination before terminating the sieve and is calibrated such that it linearly multiplies one plus the number of smooth prime columns in the Gaussian elimination matrix (for each `1.0` increment). So long as this setting is appropriately low enough, `sieving_bound_multiplier` can be set basically arbitrarily high.
 - `check_small_factors` (default value: `False`): `True` performs initial-phase trial division up to the smoothness bound, and `False` skips it.
 
 All variables defaults can also be controlled by environment variables: