  Warm Neptunes offer a rich opportunity for understanding exo-atmospheric
chemistry. With the upcoming James Webb Space Telescope (JWST), there is a need
to elucidate the balance between investments in telescope time versus
scientific yield. We use the supervised machine learning method of the random
forest to perform an information content analysis on a 11-parameter model of
transmission spectra from the various NIRSpec modes. The three bluest
medium-resolution NIRSpec modes (0.7 - 1.27 microns, 0.97 - 1.84 microns, 1.66
- 3.07 microns) are insensitive to the presence of CO. The reddest
medium-resolution mode (2.87 - 5.10 microns) is sensitive to all of the
molecules assumed in our model: CO, CO2, CH4, C2H2, H2O, HCN and NH3. It
competes effectively with the three bluest modes on the information encoded on
cloud abundance and particle size. It is also competitive with the
low-resolution prism mode (0.6 - 5.3 microns) on the inference of every
parameter except for the temperature and ammonia abundance. We recommend
astronomers to use the reddest medium-resolution NIRSpec mode for studying the
atmospheric chemistry of 800-1200 K warm Neptunes; its corresponding
high-resolution counterpart offers diminishing returns. We compare our findings
to previous JWST information content analyses that favor the blue orders, and
suggest that the reliance on chemical equilibrium could lead to biased outcomes
if this assumption does not apply. A simple, pressure-independent diagnostic
for identifying chemical disequilibrium is proposed based on measuring the
abundances of H2O, CO and CO2.
