I'm especially interested in feedback on the modeling choices — prevalence curves, specificity sliders, and whether (and how) to add correlated errors or adversarial behavior.
If anyone works in health tech, security, or trust & safety, I'd also love to hear how this maps to your practical experience with low-prevalence detection.
If you test the simulator, I'd love to hear what surprised you most — especially if something behaved differently than you expected. Thanks in advance!
An interactive simulator for visualizing how false positives explode in mass screenings for low-prevalence problems. Relevant across diverse domains including security (e.g., Chat Control; iBorderCtrl), medicine (e.g., mammography; PSA testing), and so much more.
The purpose is to begin to more easily visualize how prevalence, sensitivity, specificity, investigative capacity, and strategic behavior interact to shape real-world outcomes.
A lot of what's changed isn't parenting instincts but the systems kids are embedded in. Traffic speed, liability fears, school policies, and surveillance culture all push toward zero-risk tolerance. When the background structure punishes normal exploration, parents behave accordingly. Freedom and safety aren't individual traits; they're properties of an environment.
One underappreciated challenge in geothermal is simply the distribution of usable rock formations — low-prevalence resources. Better drilling reduces the rarity penalty by making more sites viable. Similar structure to many screening problems: improve detection and you can shift what counts as "reachable."
Medieval life lacked precision measurement: no clocks, no KPIs, no micro-evaluation. Much of modern stress isn't workload but being quantified. That shift from 'work until the task is done' to 'work until the metric is satisfied' changes everything.
You seem to be saying medieval life also lacked stress. At any moment: an army could invade and feed itself off of your livestock and grain stores; a drought could guarantee mass starvation in the winter (or sooner); disease could strike down someone you love, and progress infecting others; your own livestock could trample and kill your smaller children (thus city laws against letting hogs forage in-town); you could only put by 99% of the food you will need this winter; etc.
Oh, and a devil might jump out of the shadows, or a witch might kidnap your child, or a fairy, or...
I guess maybe we replaced outright fear with stress.
The directional‐ablation approach in Heretic is clever: by identifying residual “refusal directions” and ablating them, they shift the trade-off frontier for the model. In rare‐event screening terms: they’re effectively changing the detection threshold geometry rather than trying just to get better data. It resonates with how improving a test’s accuracy in low-prevalence settings often fails unless you address threshold + base rate.
So one thing missing from the excitement around this line of work: how little these worm effects generalize to mammals.
C. elegans has very unusual biology — direct soma→germline communication pathways, minimal nervous systems, and short generational cycles. Epigenetic inheritance is much easier to observe there than in mice or humans, where mechanisms differ and dilution across meiosis tends to erase these “marks.”
This means that, even if the PA14 avoidance effect replicates, it’s not evidence that humans inherit learned behaviours. It’s evidence that worms are an interesting edge-case system.
The article is about how difficult it is to study the effect in very simple organisms, and your criticism is that it's not yet studied in much more complicated organisms. Guess we need a few more decades (or centuries)...
No. Science is inherently human-centric. If a line of academic research has no conceivable way of benefitting humans, at least indirectly, it is eventually halted.
If an opaque actor can fabricate legal-ish complaints and pressure DNS providers into blocking a site, the system is wide open for abuse. Smaller services without legal teams would just fold.
Curious if others are seeing this kind of “shadow regulation” pop up more frequently elsewhere — especially in email filtering, CDN layers, and AI content moderation.
The difference between bonobos and chimps are genetics and not culture, you can't train chimps to live like bonobos and vice versa.
Us humans still has the genes that made us conquer and enslave the whole world, every single human culture that has ever existed enslave and murder animals, as we needed to do that to survive. You ain't gonna change those genes, so we just have to do the best we can with the genes we have and our genes are like Chimpanzees in that we want to murder and eat and exploit others, without that humans didn't get b12 and died out, so all our ancestors lived that way.
Farming is an invention, the majority of human history was spent without farming of any kind, something like 80% of human history was in the hunter/gatherer phase.
We should not underestimate the fact that where we excel is that we are better at passing off information to our offspring. This makes improvement over long periods possible as we can build off the backs of our ancestors.
If anyone works in health tech, security, or trust & safety, I'd also love to hear how this maps to your practical experience with low-prevalence detection.
If you test the simulator, I'd love to hear what surprised you most — especially if something behaved differently than you expected. Thanks in advance!