  Despite achieving sensitivities capable of detecting the extremely small
amplitude of gravitational waves (GWs), LIGO and Virgo detector data contain
frequent bursts of non-Gaussian transient noise, commonly known as 'glitches'.
Glitches come in various time-frequency morphologies, and they are particularly
challenging when they mimic the form of real GWs. Given the higher expected
event rate in the next observing run (O4), LIGO-Virgo GW event candidate
validation will require increased levels of automation. Gravity Spy, a machine
learning tool that successfully classified common types of LIGO and Virgo
glitches in previous observing runs, has the potential to be restructured as a
signal-vs-glitch classifier to accurately distinguish between glitches and GW
signals. A signal-vs-glitch classifier used for automation must be robust and
compatible with a broad array of background noise, new sources of glitches, and
the likely occurrence of overlapping glitches and GWs. We present GSpyNetTree,
the Gravity Spy Convolutional Neural Network Decision Tree: a multi-CNN
classifier using CNNs in a decision tree sorted via total GW candidate mass
tested under these realistic O4-era scenarios.
