Drum Breach: Typo Politics, Wildfire Futures, and WIPP’s Kitty Litter Nuclear Waste Accident

December 1, 2021

12pm Virtual | USC Doheny Memorial Library 241

Location: Virtual or attend in person at the USC Doheny Memorial Library 241 or via Zoom.

Click here to register and receive your Zoom link. Please RSVP by 5:00 p.m. on Tuesday, November 30 to receive the Zoom link.

If attending in person, Trojan Check verification is required for all in-person events. Masks are required indoors.

In February 2014 at the WIPP transuranic waste repository in New Mexico, a drum erupted in fire. It exposed 22 people to radiation, shut down the underground facility for 35 months, and cost the United States over a billion dollars. Heat and pressure had built up in the drum due to chemical reactions with an organic kitty litter, Swheat Scoop, which had been mistakenly added to it at Los Alamos National Laboratory: the birthplace of the atomic bomb. This talk disrupts two prominent narratives: (a) that the accident was induced by a typo made after a waste packaging operations supervisor misheard ‘inorganic kitty litter’ as ‘an organic kitty litter’ during a meeting, and (b) that it was induced primarily by ‘mismanagement’ at WIPP, Los Alamos, and the U.S. Department of Energy (DOE)’s New Mexico field offices. It does so by exploring how a series of overambitious technopolitical initiatives, fraught labor relationships, financialized subcontracting arrangements, and DOE performance incentives set the stage for Los Alamos’s notorious error by accelerating US waste packaging, shipping, and repository emplacement rates beyond systemic capacity. Attention to these operational temporalities shows how an often-overlooked nexus of schedule pressures, political-economic imperatives, and regulatory breakdowns converged to modulate nuclear waste management workflows and, ultimately, trigger a radiological accident.

The event is co-sponsored by the USC Center on Science, Technology, and Public Life, the Berggruen Institute, the USC Spatial Sciences Institute, USC Department of History, and the USC Spatial History Research Group.


composed by Arswain
machine learning consultation by Anna Tskhovrebov
commissioned by the Berggruen Institute
premiered at the Bradbury Building
downtown Los Angeles
april 22, 2022

Human perception of what sounds “beautiful” is necessarily biased and exclusive. If we are to truly expand our hearing apparatus, and thus our notion of beauty, we must not only shed preconceived sonic associations but also invite creative participation from beings non-human and non-living. We must also begin to cede creative control away from ourselves and toward such beings by encouraging them to exercise their own standards of beauty and collaborate with each other.

Movement I: Alarm Call
‘Alarm Call’ is a long-form composition and sound collage that juxtaposes, combines, and manipulates alarm calls from various human, non-human, and non-living beings. Evolutionary biologists understand the alarm call to be an altruistic behavior between species, who, by warning others of danger, place themselves by instinct in a broader system of belonging. The piece poses the question: how might we hear better to broaden and enhance our sense of belonging in the universe? Might we behave more altruistically if we better heed the calls of – and call out to – non-human beings?

Using granular synthesis, biofeedback, and algorithmic modulation, I fold the human alarm call – the siren – into non-human alarm calls, generating novel “inter-being” sonic collaborations with increasing sophistication and complexity. 

Movement II: A.I.-Truism
A synthesizer piece co-written with an AI in the style of Vangelis’s Blade Runner score, to pay homage to the space of the Bradbury Building.

Movement III: Alarmism
A machine learning model “learns” A.I.Truism and recreates Alarm Call, generating an original fusion of the two.

Movement IV: A.I. Call
A machine learning model “learns” Alarm Call and recreates A.I.Truism, generating an original fusion of the two.


RAVE (IRCAM 2021) https://github.com/acids-ircam/RAVE