I guess they must be a loss leader, it’s not like their only source of anomalous stuff is stealing from the Foundation.
I’m filtering out any items which have more than five bits on in the binary section. Two rows are likely corrupted by infohazards—I thought it might have been time travel, but seeing as they also have every bit set to 1 EXCEPT for the infohazardous bit, they stand out like a sore thumb. I found those first two by plotting SCP number VS time and noting the outliers. They’re also the only times an item was acquired when no team was sent. There are others that I’m on the fence about but I think eliminating those with excessive intersections is a reasonable data cleanup decision. Plotting year VS row number has a few jumps back but time travel’s a thing in this setting so whatever.
Not sure where the nonlinearity here comes from. Maybe the simulation has a certain number of SCP numbers assigned at any given time, and then randomly draws from them as targets.
Splitting cumulative profits out by both SEK classification and site number shows variations between sites, though I’m not normalising here for the number of times a site was targeted or the SEK distributions at any given site.
Okay, this comment’s for stuff I found. Will edit this post with things as I find them.
MC&D when they burn millions to acquire keter items https://i.imgur.com/GlTx4HQ.png
I guess they must be a loss leader, it’s not like their only source of anomalous stuff is stealing from the Foundation.
I’m filtering out any items which have more than five bits on in the binary section. Two rows are likely corrupted by infohazards—I thought it might have been time travel, but seeing as they also have every bit set to 1 EXCEPT for the infohazardous bit, they stand out like a sore thumb. I found those first two by plotting SCP number VS time and noting the outliers. They’re also the only times an item was acquired when no team was sent. There are others that I’m on the fence about but I think eliminating those with excessive intersections is a reasonable data cleanup decision. Plotting year VS row number has a few jumps back but time travel’s a thing in this setting so whatever.
Not sure where the nonlinearity here comes from. Maybe the simulation has a certain number of SCP numbers assigned at any given time, and then randomly draws from them as targets.
Splitting cumulative profits out by both SEK classification and site number shows variations between sites, though I’m not normalising here for the number of times a site was targeted or the SEK distributions at any given site.