The method developed by the team led by Brown and Yu, iterative Random Forests (iRF), builds on an algorithm called random forests, a popular and effective predictive modeling tool,
translating the internal states of the black box learner into a human - interpretable form.
It might help you if you had a few concepds in mind too when considering this subject, like «space» is the big energy «sink» with old sol (and the
internal heat generating processes (including nuclear) of the earth) as sources... any mechanism that results in a delay of energy leaving earth, such as a «bounce - back» or a re-rad of energy (like back radiation) certainly is going to increase the «energy flux» in the system, and this in any way you want to frame the argument
translates to a «higher» energy
state, and a higher so - called temperature» (movement in matter, velocity of air molecules or oscillations in certain «resonant molecules) as well.